Table 1: Patient characteristics

Patient characteristics by state (general population) or transition (from general population, diagnosed with COVID-19, and hospitalised with COVID-19)

table1.data<-rbind(  
# all those in general population
  r.healthy.diagnosis%>% 
    mutate(group="General population"), 
# all those who transition from general population to diagnosed
  r.healthy.diagnosis%>% 
    filter(status==1) %>% 
    mutate(group="From general population to diagnosed with COVID-19"),
# all those who transition from general population to hospitalised
  r.healthy.hospitalised%>% 
    filter(status==1) %>% 
    mutate(group="From general population to hospitalised with COVID-19"), 
# all those who transition from diagnosed to hospitalised
  r.diagnosis.hospitalised %>% 
    filter(status==1) %>% 
    mutate(group="From diagnosed with COVID-19 to hospitalised with COVID-19"), 
# all those who transition from hospitalised to death
  r.hospitalised.death %>% 
    filter(status==1) %>% 
    mutate(group="From hospitalised with COVID-19 to death"), 
# # all those who transition from general population to death
 r.healthy.death %>%
   filter(status==1) %>%
   mutate(group="From general population to death"),
# all those who transition from diagnosed to death
 r.diagnosis.death %>% 
   filter(status==1) %>% 
   mutate(group="From diagnosed with COVID-19 to death")) %>% 
   mutate(group=factor(group,
                      levels=c("General population",
                               "From general population to diagnosed with COVID-19",
                               "From general population to hospitalised with COVID-19",
                                "From general population to death",
                               "From diagnosed with COVID-19 to hospitalised with COVID-19",
                               "From diagnosed with COVID-19 to death",
                               "From hospitalised with COVID-19 to death"
                               ))) %>%
  mutate(gender=factor(gender,
                       levels=c("Male", "Female")))


# variables for table 1
vars<-c("age",
        "age_gr",
        "gender",
        "charlson",
        # components of charlson
        "Myocardial_infarction",
        "Congestive_heart_failure",
        "Peripheral_vascular_disease",
        "Cerebrovascular_disease",
        "Dementia",
        "Chronic_pulmonary_disease",
        "Rheumatologic_disease",
        "Peptic_ulcer_disease",
        "Mild_liver_disease",
        "Diabetes_with_chronic_complications",
        "Hemoplegia_or_paralegia",
        "Renal_disease",
        "Any_malignancy",
        "Moderate_to_severe_liver_disease",
        "Metastatic_solid_tumor",
        "AIDS",
        # atlas cohorts
        "a_autoimmune_condition",
         "a_chronic_kidney_disease",
         "a_copd",
         "a_dementia",
         "a_heart_disease",
         "a_hyperlipidemia",
         "a_hypertension",
         "a_malignant_neoplasm",
         "a_obesity.5y",
         "a_t2_diabetes")

factor.vars<- c("age_gr",
                "gender",
                "charlson",
                "Myocardial_infarction",
                "Congestive_heart_failure",
                "Peripheral_vascular_disease",
                "Cerebrovascular_disease",
                "Dementia",
                "Chronic_pulmonary_disease",
                "Rheumatologic_disease",
                "Peptic_ulcer_disease",
                "Mild_liver_disease",
                "Diabetes_with_chronic_complications",
                "Hemoplegia_or_paralegia",
                "Renal_disease",
                "Any_malignancy",
                "Moderate_to_severe_liver_disease",
                "Metastatic_solid_tumor",
                "AIDS",
                "a_autoimmune_condition",
         "a_chronic_kidney_disease",
         "a_copd",
         "a_dementia",
         "a_heart_disease",
         "a_hyperlipidemia",
         "a_hypertension",
         "a_malignant_neoplasm",
        "a_obesity.5y",
         "a_t2_diabetes" )

summary.characteristics<-print(CreateTableOne(
  vars =  vars,
  factorVars = factor.vars,
  includeNA=T,
  data = table1.data,
  strata=c("group"),
  test = F), 
  showAllLevels=F,smd=F,
  nonnormal = vars, #all 
  noSpaces = TRUE,
  contDigits = 1,
  printToggle=FALSE)


# format numbers (eg commas etc)  
# functionality does not seem to be in tableone package
# so do this manually
for(i in 1:ncol(summary.characteristics)) {
# tidy up 
  cur_column <- summary.characteristics[, i]
  cur_column <- str_extract(cur_column, '[0-9.]+\\b') %>% 
                as.numeric() 
  cur_column <-nice.num(cur_column)
  # add back in
  summary.characteristics[, i] <- str_replace(string=summary.characteristics[, i], 
                              pattern='[0-9.]+\\b', 
                              replacement=cur_column)    
}

# names
#rownames(summary.characteristics)

rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), "charlson", "charlson comorbidity index")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), "obesity.5y", "obesity")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), " = 1", "")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics), "a_", "")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "_", " ")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "_", " ")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "t2", "Type 2")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics) , "copd", "COPD")
rownames(summary.characteristics)<-str_to_sentence(rownames(summary.characteristics))
From general population
From diagnosed with COVID-19
From hospitalised with COVID-19
General population To diagnosed with COVID-19 To hospitalised with COVID-19 To death To hospitalised with COVID-19 To death To death
5,627,520 109,367 8,582 11,726 9,437 2,794 2,791
44 [25.0, 60.0] 47 [36.0, 61.0] 72 [58.2, 81.0] 85 [76.0, 90.0] 61 [50.0, 75.0] 87 [81.0, 91.0] 81 [73.0, 86.5]
967,227 (17.2) 4,547 (4.2) 34 (0.4) 9 (0.1) 40 (0.4) <5 <5
1,438,732 (25.6) 30,640 (28.0) 432 (5.0) 80 (0.7) 857 (9.1) 5 (0.2) 11 (0.4)
1,788,832 (31.8) 44,803 (41.0) 1,815 (21.1) 658 (5.6) 3,477 (36.8) 63 (2.3) 143 (5.1)
617,929 (11.0) 10,661 (9.7) 1,590 (18.5) 1,034 (8.8) 1,708 (18.1) 136 (4.9) 296 (10.6)
474,369 (8.4) 7,163 (6.5) 2,252 (26.2) 1,999 (17.0) 1,702 (18.0) 421 (15.1) 840 (30.1)
340,431 (6.0) 11,553 (10.6) 2,459 (28.7) 7,946 (67.8) 1,653 (17.5) 2,169 (77.6) 1,500 (53.7)
2,859,274 (50.8) 64,473 (59.0) 3,770 (43.9) 5,932 (50.6) 4,357 (46.2) 1,625 (58.2) 1,122 (40.2)
4,572,265 (81.2) 81,833 (74.8) 3,750 (43.7) 1,672 (14.3) 5,513 (58.4) 391 (14.0) 604 (21.6)
410,497 (7.3) 10,708 (9.8) 1,156 (13.5) 1,724 (14.7) 1,157 (12.3) 566 (20.3) 447 (16.0)
363,057 (6.5) 8,211 (7.5) 1,518 (17.7) 2,398 (20.5) 1,266 (13.4) 507 (18.1) 572 (20.5)
281,701 (5.0) 8,615 (7.9) 2,158 (25.1) 5,932 (50.6) 1,501 (15.9) 1,330 (47.6) 1,168 (41.8)
Components of Charlson Comorbidity Index
56,855 (1.0) 1,238 (1.1) 364 (4.2) 687 (5.9) 249 (2.6) 137 (4.9) 166 (5.9)
74,650 (1.3) 2,574 (2.4) 743 (8.7) 2,383 (20.3) 454 (4.8) 450 (16.1) 424 (15.2)
65,165 (1.2) 1,576 (1.4) 505 (5.9) 1,143 (9.7) 303 (3.2) 191 (6.8) 250 (9.0)
69,629 (1.2) 2,229 (2.0) 434 (5.1) 1,097 (9.4) 343 (3.6) 281 (10.1) 238 (8.5)
58,138 (1.0) 4,917 (4.5) 608 (7.1) 2,972 (25.3) 490 (5.2) 1,154 (41.3) 423 (15.2)
315,854 (5.6) 8,074 (7.4) 1,349 (15.7) 2,354 (20.1) 1,047 (11.1) 473 (16.9) 593 (21.2)
46,567 (0.8) 1,233 (1.1) 274 (3.2) 429 (3.7) 184 (1.9) 90 (3.2) 114 (4.1)
104,894 (1.9) 2,657 (2.4) 412 (4.8) 739 (6.3) 397 (4.2) 164 (5.9) 157 (5.6)
53,311 (0.9) 1,264 (1.2) 191 (2.2) 444 (3.8) 177 (1.9) 49 (1.8) 75 (2.7)
76,214 (1.4) 1,932 (1.8) 562 (6.5) 980 (8.4) 372 (3.9) 220 (7.9) 259 (9.3)
9,995 (0.2) 432 (0.4) 64 (0.7) 184 (1.6) 49 (0.5) 60 (2.1) 46 (1.6)
204,825 (3.6) 5,839 (5.3) 1,581 (18.4) 3,835 (32.7) 1,072 (11.4) 953 (34.1) 864 (31.0)
354,645 (6.3) 8,526 (7.8) 1,783 (20.8) 4,282 (36.5) 1,397 (14.8) 736 (26.3) 825 (29.6)
8,673 (0.2) 181 (0.2) 61 (0.7) 163 (1.4) 40 (0.4) 11 (0.4) 31 (1.1)
8,732 (0.2) 185 (0.2) 60 (0.7) 530 (4.5) 34 (0.4) 31 (1.1) 30 (1.1)
18,226 (0.3) 421 (0.4) 42 (0.5) 44 (0.4) 48 (0.5) <5 <5
277,450 (4.9) 6,936 (6.3) 871 (10.1) 1,320 (11.3) 786 (8.3) 291 (10.4) 340 (12.2)
Other conditions of interest
211,842 (3.8) 5,843 (5.3) 1,548 (18.0) 3,844 (32.8) 1,058 (11.2) 955 (34.2) 858 (30.7)
122,745 (2.2) 2,889 (2.6) 801 (9.3) 1,488 (12.7) 515 (5.5) 280 (10.0) 379 (13.6)
57,310 (1.0) 4,795 (4.4) 598 (7.0) 2,894 (24.7) 486 (5.1) 1,108 (39.7) 416 (14.9)
547,795 (9.7) 13,540 (12.4) 2,846 (33.2) 5,865 (50.0) 2,132 (22.6) 1,264 (45.2) 1,371 (49.1)
520,837 (9.3) 11,888 (10.9) 1,590 (18.5) 1,630 (13.9) 1,599 (16.9) 415 (14.9) 523 (18.7)
703,167 (12.5) 16,400 (15.0) 2,609 (30.4) 3,971 (33.9) 2,412 (25.6) 970 (34.7) 969 (34.7)
297,409 (5.3) 6,656 (6.1) 1,405 (16.4) 3,818 (32.6) 1,075 (11.4) 626 (22.4) 691 (24.8)
937,593 (16.7) 22,493 (20.6) 3,384 (39.4) 3,251 (27.7) 3,286 (34.8) 733 (26.2) 1,118 (40.1)
324,944 (5.8) 7,427 (6.8) 1,773 (20.7) 2,684 (22.9) 1,422 (15.1) 637 (22.8) 734 (26.3)

Figure 1: Age and gender histogram

As above, by state/ transition, transition 3 (general pop to death omitted as not of primary interest)

plot.data<-table1.data
# to split across lines
plot.data$group<-plyr::revalue(plot.data$group, c("From general population to diagnosed with COVID-19"=
                                     "From general population\nto diagnosed with COVID-19", 
                                   "From general population to hospitalised with COVID-19"=
                                     "From general population\nto hospitalised with COVID-19",
                                   "From general population to death"=
                                     "From general population\nto death",
                                   "From diagnosed with COVID-19 to hospitalised with COVID-19"=
                                     "From diagnosed with COVID-19\nto hospitalised with COVID-19",
                                   "From diagnosed with COVID-19 to death"=
                                   "From diagnosed with COVID-19\nto death",
                                   "From hospitalised with COVID-19 to death"=
                                     "From hospitalised with COVID-19\nto death")) 
#Mirrored
plot.data.male<-plot.data %>% 
  filter(gender=="Male") %>% 
  filter(group!="From general population\nto death")
plot.data.female<-plot.data %>%
  filter(gender=="Female") %>% 
  filter(group!="From general population\nto death")


dat_text <- data.frame(
  label = c("Female", "Male"),
  group   = factor(c("General population","General population")),
  x     = c(75,75),
  y     = c(-0.04, 0.035)
)


histogram3<-ggplot() + 
  geom_histogram(data=plot.data.male ,
                 aes(x=age, y=..density..),
                 colour="black", 
                 binwidth = 4, boundary = 0,
                 fill="#F21A00")+
  geom_histogram(aes(x=age, y=-..density..),
                 colour="black", 
                 binwidth = 4, boundary = 0,
                 data=plot.data.female,
                 fill="#3B9AB2")+
coord_flip()+
  facet_wrap(group ~.)+
  theme_bw()+
  scale_y_continuous(breaks=c(-0.1,-0.05, 0,0.05,0.1))+
  xlim(0,104)+
  geom_label(
    data    = dat_text,
    mapping = aes(x = x,
                  y = y, label = label),
    size=5)+
  theme(legend.title = element_blank(),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14,face="bold"),
        strip.text = element_text(size=12, face="bold"),
        strip.background = element_rect( fill="#f7f7f7"),
        legend.text=element_text(size=14),
        legend.position="top") +
  ylab("Density")+
  xlab("Age")


ggsave( "histogram3.png",histogram3,
        dpi=300,
        width = 11, height = 7)

include_graphics("histogram3.png")

Figure 2: Conditions by age group and state/ tranistion

a<-table1.data %>% 
  group_by(gender, group) %>% 
  add_tally() %>% 
  group_by(gender, group, n) %>% 
  summarise(a_autoimmune_condition=sum(a_autoimmune_condition==1),
           a_chronic_kidney_disease =sum(a_chronic_kidney_disease==1),
           a_copd =sum(a_copd==1),
           a_dementia =sum(a_dementia==1),
           a_heart_disease =sum(a_heart_disease==1),
          a_hyperlipidemia  =sum(a_hyperlipidemia==1),
           a_hypertension =sum(a_hypertension==1),
          a_malignant_neoplasm = sum(a_malignant_neoplasm==1),
          a_obesity.5y = sum(a_obesity.5y==1),
         a_t2_diabetes  =sum(a_t2_diabetes==1)) %>% 
  mutate(
   a_autoimmune_condition=a_autoimmune_condition/n,
           a_chronic_kidney_disease =a_chronic_kidney_disease/n,
           a_copd =a_copd/n,
           a_dementia =a_dementia/n,
           a_heart_disease =a_heart_disease/n,
          a_hyperlipidemia  =a_hyperlipidemia/n,
           a_hypertension =a_hypertension/n,
          a_malignant_neoplasm = a_malignant_neoplasm/n,
          a_obesity.5y = a_obesity.5y/n,
          a_t2_diabetes = a_t2_diabetes/n) %>% 
   pivot_longer(
   cols = starts_with("a_"),
   names_to = "var",
   values_to = "prop",
   values_drop_na = TRUE)

a$group<-plyr::revalue(a$group, c("From general population to diagnosed with COVID-19"=
                                     "From general\npopulation to\ndiagnosed with\nCOVID-19", 
                                   "From general population to hospitalised with COVID-19"=
                                     "From general\npopulation\nto hospitalised\nwith COVID-19",
                                   "From general population to death"=
                                     "From general\npopulation\nto death",
                                   "From diagnosed with COVID-19 to hospitalised with COVID-19"=
                                     "From diagnosed\nwith COVID-19\nto hospitalised\nwith COVID-19",
                                   "From diagnosed with COVID-19 to death"=
                                   "From diagnosed\nwith COVID-19\nto death",
                                   "From hospitalised with COVID-19 to death"=
                                     "From hospitalised\nwith COVID-19\nto death")) 
gg.conditions2<-
  a %>% 
    mutate(var.name=
             ifelse(
      var=="a_autoimmune_condition",
    "Autoimmune condition",
    ifelse(
      var=="a_chronic_kidney_disease",
    "Chronic kidney disease",
               ifelse(
      var=="a_copd",
    "COPD",
                 ifelse(
      var=="a_dementia",
    "Dementia",
                 ifelse(
      var=="a_heart_disease",
    "Heart disease",
                 ifelse(
      var=="a_hyperlipidemia",
    "Hyperlipidemia",
                 ifelse(
      var=="a_hypertension",
    "Hypertension",
                 ifelse(
      var=="a_malignant_neoplasm",
    "Malignant neoplasm",
      ifelse(
      var=="a_obesity.5y",
    "Obesity",
                 ifelse(
      var=="a_t2_diabetes",
    "Type 2 Diabetes Mellitus",NA    ))))))))))) %>% 
  filter(group!="From general\npopulation\nto death") %>% 
  ggplot()+
  facet_grid(gender~group, switch="y")+
  geom_bar(aes(var, prop, fill=var.name), 
           width = 1, colour="grey",
           stat="identity", position=position_dodge())+
  theme_minimal() +
  scale_y_continuous( breaks=c(0.1,0.2,0.3,0.4, 0.5, 0.6),
                     limits=c(-0.075,0.6))+
    scale_fill_manual(values = c("#a6cee3","#1f78b4","#b2df8a","#33a02c",
"#fb9a99","#e31a1c","#fdbf6f","#ff7f00","#cab2d6", "#6a3d9a"))+ 
   theme(panel.spacing = unit(0, "lines"),
        legend.title = element_blank(),
        strip.text = element_text(size=10, face="bold"),
         panel.grid.major.x = element_blank() ,
     axis.text.x = element_blank(),
   axis.title = element_blank(),
   axis.text.y = element_blank(),
         axis.ticks.y = element_blank(),
   strip.text.y.left = element_text(angle = 0),
   legend.position = "bottom" ) +
  geom_text(x = 3, y = 0.3,
            size=3,
            label = "30%")+
  geom_text(x = 4, y = 0.6,
            size=3,
            label = "60%")+
  coord_polar(start = 0)
  

ggsave("conditions2.png",gg.conditions2,
        dpi=300,
        width = 12, height = 7)

include_graphics("conditions2.png")

Table 2: Transitions

# events
events<-rbind(
  # overall
  r %>% 
  group_by(trans) %>% 
  add_tally() %>% 
  group_by(trans, n) %>%  
  summarise(events=sum(status),
            time.0=min(time),
            time.25=quantile(time, probs =0.25),
            time.50=quantile(time, probs =0.5),
            time.75=quantile(time, probs =0.75),
            time.1=max(time)) %>% 
  mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
  mutate(group="all") %>% 
  mutate(level="all") %>% 
  select(trans, group,level, n, time, events),
  # by age_gr
  r %>% 
  group_by(trans, age_gr) %>% 
  add_tally() %>% 
  group_by(trans, n, age_gr) %>%  
  summarise(events=sum(status),
            time.0=min(time),
            time.25=quantile(time, probs =0.25),
            time.50=quantile(time, probs =0.5),
            time.75=quantile(time, probs =0.75),
            time.1=max(time))%>% 
    mutate(age_gr = factor(age_gr, levels = c("Under 18","18 to 39", "40 to 59", "60 to 69",
                                        "70 to 79", "80 or older"))) %>% 
    arrange(trans, age_gr)  %>% 
  mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
  mutate(group="Age") %>% 
  mutate(level=age_gr) %>% 
  select(trans, group,level, n, time, events),
  # by gender
  r %>% 
    group_by(trans, gender) %>% 
    add_tally() %>% 
    group_by(trans, n, gender) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(gender = factor(gender, levels = c("Male", "Female"))) %>% 
    arrange(trans, gender)   %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Sex") %>% 
    mutate(level=gender) %>% 
    select(trans, group,level, n, time, events),
  # by charlson
  r %>% 
    group_by(trans, charlson) %>% 
    add_tally() %>% 
    group_by(trans, n, charlson) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(charlson = factor(charlson, levels = c("0", "1", "2", "3+"))) %>% 
    arrange(trans, charlson)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Charlson") %>% 
    mutate(level=charlson) %>% 
    select(trans, group,level, n, time, events),
  # by a_autoimmune_condition
  r %>% 
    group_by(trans, a_autoimmune_condition) %>% 
    add_tally() %>% 
    group_by(trans, n, a_autoimmune_condition) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_autoimmune_condition = factor(a_autoimmune_condition, levels = c("0", "1"))) %>% 
    arrange(trans, a_autoimmune_condition)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Autoimmune condition") %>% 
    mutate(level=a_autoimmune_condition) %>% 
    select(trans, group,level, n, time, events),
  
  # by a_chronic_kidney_disease
  r %>% 
    group_by(trans, a_chronic_kidney_disease) %>% 
    add_tally() %>% 
    group_by(trans, n, a_chronic_kidney_disease) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_chronic_kidney_disease = factor(a_chronic_kidney_disease, levels = c("0", "1"))) %>% 
    arrange(trans, a_chronic_kidney_disease)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Chronic kidney disease") %>% 
    mutate(level=a_chronic_kidney_disease) %>% 
    select(trans, group,level, n, time, events),
    # by a_copd
  r %>% 
    group_by(trans, a_copd) %>% 
    add_tally() %>% 
    group_by(trans, n, a_copd) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_copd = factor(a_copd, levels = c("0", "1"))) %>% 
    arrange(trans, a_copd)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="COPD") %>% 
    mutate(level=a_copd) %>% 
    select(trans, group,level, n, time, events),
  # by a_dementia
  r %>% 
    group_by(trans, a_dementia) %>% 
    add_tally() %>% 
    group_by(trans, n, a_dementia) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_dementia = factor(a_dementia, levels = c("0", "1"))) %>% 
    arrange(trans, a_dementia)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Dementia") %>% 
    mutate(level=a_dementia) %>% 
    select(trans, group,level, n, time, events),
    # by a_heart_disease
  r %>% 
    group_by(trans, a_heart_disease) %>% 
    add_tally() %>% 
    group_by(trans, n, a_heart_disease) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_heart_disease = factor(a_heart_disease, levels = c("0", "1"))) %>% 
    arrange(trans, a_heart_disease)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Heart disease") %>% 
    mutate(level=a_heart_disease) %>% 
    select(trans, group,level, n, time, events),
    # by a_hyperlipidemia
  r %>% 
    group_by(trans, a_hyperlipidemia) %>% 
    add_tally() %>% 
    group_by(trans, n, a_hyperlipidemia) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_hyperlipidemia = factor(a_hyperlipidemia, levels = c("0", "1"))) %>% 
    arrange(trans, a_hyperlipidemia)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Hyperlipidemia") %>% 
    mutate(level=a_hyperlipidemia) %>% 
    select(trans, group,level, n, time, events),
    # by a_malignant_neoplasm
  r %>% 
    group_by(trans, a_malignant_neoplasm) %>% 
    add_tally() %>% 
    group_by(trans, n, a_malignant_neoplasm) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_malignant_neoplasm = factor(a_malignant_neoplasm, levels = c("0", "1"))) %>% 
    arrange(trans, a_malignant_neoplasm)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Malignant neoplasm") %>% 
    mutate(level=a_malignant_neoplasm) %>% 
    select(trans, group,level, n, time, events),
    # by a_obesity.5y
  r %>% 
    group_by(trans, a_obesity.5y) %>% 
    add_tally() %>% 
    group_by(trans, n, a_obesity.5y) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_obesity.5y = factor(a_obesity.5y, levels = c("0", "1"))) %>% 
    arrange(trans, a_obesity.5y)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Obesity") %>% 
    mutate(level=a_obesity.5y) %>% 
    select(trans, group,level, n, time, events),
    # by a_t2_diabetes
  r %>% 
    group_by(trans, a_t2_diabetes) %>% 
    add_tally() %>% 
    group_by(trans, n, a_t2_diabetes) %>%  
    summarise(events=sum(status),
              time.0=min(time),
              time.25=quantile(time, probs =0.25),
              time.50=quantile(time, probs =0.5),
              time.75=quantile(time, probs =0.75),
              time.1=max(time))%>% 
    mutate(a_t2_diabetes = factor(a_t2_diabetes, levels = c("0", "1"))) %>% 
    arrange(trans, a_t2_diabetes)  %>% 
    mutate(time=paste0(time.50, " (", time.0, ", ", time.25, " to ", time.75, ", ", time.1, ")")) %>% 
    mutate(group="Type 2 diabetes") %>% 
    mutate(level=a_t2_diabetes) %>% 
    select(trans, group,level, n, time, events)
  
  
  )
  
# format numbers
events$n<-nice.num(events$n)
events$events<-nice.num(events$events)

# order by transition
events<-events %>% 
  arrange(trans)
# factors for covid.data ----
covid.data$age_gr <- factor(covid.data$age_gr, 
                   levels = c("Under 18","18 to 39", "40 to 59", "60 to 69",
                                        "70 to 79", "80 or older"))
covid.data$gender <- factor(covid.data$gender, 
                   levels = c("Male", "Female"))

covid.data$charlson <- factor(covid.data$charlson, 
                   levels = c("0", "1", "2", "3+"))




# get 67 day cumualative incidence for initial transitions -----
events.t1_t3<-events %>% 
  filter(trans %in% c(1:3))

#cuminc 
c.inc_fit<-list()

#overall
c.inc_fit[["overall"]] <- cuminc(ftime = covid.data$healthy_c.time, 
              fstatus = covid.data$healthy_c.event)

#by groups
groups<-c("age_gr", "gender", 
          "charlson",
          "a_autoimmune_condition",
          "a_chronic_kidney_disease", "a_copd",
          "a_dementia","a_heart_disease","a_hyperlipidemia",
          "a_hypertension", "a_malignant_neoplasm",
          "a_obesity.5y","a_t2_diabetes")




for(i in 1:length(groups)){
 message(paste0("Working on ", i, " of ", length(groups)))
   c.inc_fit[[groups[i]]]<- cuminc(ftime = covid.data$healthy_c.time,  
              fstatus = covid.data$healthy_c.event, 
            group = covid.data[[groups[i]]]) 
}

# extract estimates at 67 days
tp<-list()
tp[["overall"]]<-timepoints(c.inc_fit[["overall"]],67)$est

for(i in 1:length(groups)){
   tp[[groups[i]]]<- timepoints(c.inc_fit[[groups[i]]],67)$est

}

t1<-data.frame( 
  trans=1,
  group=c("all", rep("Age",6), 
          rep("Sex",2), 
          rep("Charlson",4),
          rep("Autoimmune condition",2),
          rep("Chronic kidney disease",2),
          rep("COPD",2),
          rep("Dementia",2),
          rep("Heart disease",2),
          rep("Hyperlipidemia",2),
          rep("Malignant neoplasm",2),
          rep("Obesity",2),
          rep("Type 2 diabetes",2)
          ),
   level=c("all",
           levels(covid.data$age_gr),
           levels(covid.data$gender),
           levels(covid.data$charlson),
           "0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
           "0", "1","0", "1","0", "1"),
est=c(
tp$overall[1],
tp$age_gr[1:6],
tp$gender[1:2],
tp$charlson[1:4],
tp$a_autoimmune_condition[1:2],
tp$a_chronic_kidney_disease[1:2],
tp$a_copd[1:2],
tp$a_dementia[1:2],
tp$a_heart_disease[1:2],
tp$a_hyperlipidemia[1:2],
tp$a_malignant_neoplasm[1:2],
tp$a_obesity.5y[1:2],
tp$a_t2_diabetes[1:2]
))

t2<-data.frame( 
  trans=2,
  group=c("all", rep("Age",6), 
          rep("Sex",2), 
          rep("Charlson",4),
          rep("Autoimmune condition",2),
          rep("Chronic kidney disease",2),
          rep("COPD",2),
          rep("Dementia",2),
          rep("Heart disease",2),
          rep("Hyperlipidemia",2),
          rep("Malignant neoplasm",2),
          rep("Obesity",2),
          rep("Type 2 diabetes",2)
          ),
   level=c("all",
           levels(covid.data$age_gr),
           levels(covid.data$gender),
           levels(covid.data$charlson),
           "0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
           "0", "1","0", "1","0", "1"),
est=c(
tp$overall[2],
tp$age_gr[7:12],
tp$gender[3:4],
tp$charlson[5:8],
tp$a_autoimmune_condition[3:4],
tp$a_chronic_kidney_disease[3:4],
tp$a_copd[3:4],
tp$a_dementia[3:4],
tp$a_heart_disease[3:4],
tp$a_hyperlipidemia[3:4],
tp$a_malignant_neoplasm[3:4],
tp$a_obesity.5y[3:4],
tp$a_t2_diabetes[3:4]
))

t3<-data.frame( 
  trans=3,
   group=c("all", rep("Age",6), 
          rep("Sex",2), 
          rep("Charlson",4),
          rep("Autoimmune condition",2),
          rep("Chronic kidney disease",2),
          rep("COPD",2),
          rep("Dementia",2),
          rep("Heart disease",2),
          rep("Hyperlipidemia",2),
          rep("Malignant neoplasm",2),
          rep("Obesity",2),
          rep("Type 2 diabetes",2)
          ),
   level=c("all",
           levels(covid.data$age_gr),
           levels(covid.data$gender),
           levels(covid.data$charlson),
           "0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
           "0", "1","0", "1","0", "1"),
est=c(
tp$overall[3],
tp$age_gr[13:18],
tp$gender[5:6],
tp$charlson[9:12],
tp$a_autoimmune_condition[5:6],
tp$a_chronic_kidney_disease[5:6],
tp$a_copd[5:6],
tp$a_dementia[5:6],
tp$a_heart_disease[5:6],
tp$a_hyperlipidemia[5:6],
tp$a_malignant_neoplasm[5:6],
tp$a_obesity.5y[5:6],
tp$a_t2_diabetes[5:6]
))

est<-rbind(t1,t2,t3)


# add to table
events.t1_t3<-events.t1_t3 %>% 
  left_join(est,
            by = c("trans","group", "level"))
# format number
events.t1_t3<-events.t1_t3 %>% 
  mutate(est=paste0(nice.num2(est*100), "%"))

# pivot wide- transitions on same row
events.t1_t3.wide<-events.t1_t3 %>% 
  pivot_wider(names_from = trans,
    values_from = c(events, est)) 

# formatting
events.t1_t3.wide<-events.t1_t3.wide %>% 
  mutate(events_diag.general.pop=paste0(events_1, " (", est_1, ")")) %>% 
  mutate(events_hosp.general.pop=paste0(events_2, " (", est_2, ")")) %>% 
  mutate(events_death.general.pop=paste0(events_3, " (", est_3, ")")) %>% 
  select(group, level, n, time, events_diag.general.pop, events_hosp.general.pop, events_death.general.pop) %>% 
  rename( n.general.pop=n,
          time.general.pop=time)
# get 45 day cumualative incidence for transitions from diagnosis ----
events.t4_t5<-events %>% 
  filter(trans %in% c(4:5))


#cuminc 
c.inc_fit<-list()

quiet <- function(x) {
  sink(tempfile())
  on.exit(sink())
  invisible(force(x))
} 
# use quiet to suppress message about missing values (we only want pop for specific state, others are missing)


#overall
c.inc_fit[["overall"]] <- quiet(cuminc(ftime = covid.data$diagnosis_c.time, 
              fstatus = covid.data$diagnosis_c.event))

#by groups
groups<-c("age_gr", "gender", 
          "charlson",
          "a_autoimmune_condition",
          "a_chronic_kidney_disease", "a_copd",
          "a_dementia","a_heart_disease","a_hyperlipidemia",
          "a_hypertension", "a_malignant_neoplasm",
          "a_obesity.5y","a_t2_diabetes")




for(i in 1:length(groups)){
 message(paste0("Working on ", i, " of ", length(groups)))
   c.inc_fit[[groups[i]]]<- quiet(cuminc(ftime = covid.data$diagnosis_c.time,  
              fstatus = covid.data$diagnosis_c.event, 
            group = covid.data[[groups[i]]]) )
  
}




# extract estimates at 45 days
tp<-list()
tp[["overall"]]<-timepoints(c.inc_fit[["overall"]],45)$est

for(i in 1:length(groups)){
   tp[[groups[i]]]<- timepoints(c.inc_fit[[groups[i]]],45)$est

}

t1<-data.frame( 
  trans=4,
  group=c("all", rep("Age",6), 
          rep("Sex",2), 
          rep("Charlson",4),
          rep("Autoimmune condition",2),
          rep("Chronic kidney disease",2),
          rep("COPD",2),
          rep("Dementia",2),
          rep("Heart disease",2),
          rep("Hyperlipidemia",2),
          rep("Malignant neoplasm",2),
          rep("Obesity",2),
          rep("Type 2 diabetes",2)
          ),
   level=c("all",
           levels(covid.data$age_gr),
           levels(covid.data$gender),
           levels(covid.data$charlson),
           "0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
           "0", "1","0", "1","0", "1"),
est=c(
tp$overall[1],
tp$age_gr[1:6],
tp$gender[1:2],
tp$charlson[1:4],
tp$a_autoimmune_condition[1:2],
tp$a_chronic_kidney_disease[1:2],
tp$a_copd[1:2],
tp$a_dementia[1:2],
tp$a_heart_disease[1:2],
tp$a_hyperlipidemia[1:2],
tp$a_malignant_neoplasm[1:2],
tp$a_obesity.5y[1:2],
tp$a_t2_diabetes[1:2]
))

t2<-data.frame( 
  trans=5,
  group=c("all", rep("Age",6), 
          rep("Sex",2), 
          rep("Charlson",4),
          rep("Autoimmune condition",2),
          rep("Chronic kidney disease",2),
          rep("COPD",2),
          rep("Dementia",2),
          rep("Heart disease",2),
          rep("Hyperlipidemia",2),
          rep("Malignant neoplasm",2),
          rep("Obesity",2),
          rep("Type 2 diabetes",2)
          ),
   level=c("all",
           levels(covid.data$age_gr),
           levels(covid.data$gender),
           levels(covid.data$charlson),
           "0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
           "0", "1","0", "1","0", "1"),
est=c(
tp$overall[2],
tp$age_gr[7:12],
tp$gender[3:4],
tp$charlson[5:8],
tp$a_autoimmune_condition[3:4],
tp$a_chronic_kidney_disease[3:4],
tp$a_copd[3:4],
tp$a_dementia[3:4],
tp$a_heart_disease[3:4],
tp$a_hyperlipidemia[3:4],
tp$a_malignant_neoplasm[3:4],
tp$a_obesity.5y[3:4],
tp$a_t2_diabetes[3:4]
))

est<-rbind(t1,t2)
##



# add to table
events.t4_t5<-events.t4_t5 %>% 
  left_join(est,
            by = c("trans","group", "level"))
# format numbers
events.t4_t5<-events.t4_t5 %>% 
  mutate(est=paste0(nice.num2(est*100), "%"))
# pivot wide
events.t4_t5.wide<-events.t4_t5 %>% 
  pivot_wider(names_from = trans,
    values_from = c(events, est))

events.t4_t5.wide<-events.t4_t5.wide %>% 
  mutate(events_hosp.diag=paste0(events_4, " (", est_4, ")")) %>% 
  mutate(events_death.diag=paste0(events_5, " (", est_5, ")")) %>% 
  select(group, level, n, time, events_hosp.diag, events_death.diag) %>% 
  rename(n.diag=n,
        time.diagn=time)
# get 45 days cumualative incidence for transition from hospitalised ----
events.t6<-events %>% 
  filter(trans %in% c(6))

#cuminc 
c.inc_fit<-list()

quiet <- function(x) {
  sink(tempfile())
  on.exit(sink())
  invisible(force(x))
} 
# use quiet to suppress message about missing values (we only want pop for specific state, others are missing)


#overall
c.inc_fit[["overall"]] <- quiet(cuminc(ftime = r.hospitalised.death$time, 
              fstatus = r.hospitalised.death$status))

#by groups
groups<-c("age_gr", "gender", 
          "charlson",
          "a_autoimmune_condition",
          "a_chronic_kidney_disease", "a_copd",
          "a_dementia","a_heart_disease","a_hyperlipidemia",
          "a_hypertension", "a_malignant_neoplasm",
          "a_obesity.5y","a_t2_diabetes")




for(i in 1:length(groups)){
 message(paste0("Working on ", i, " of ", length(groups)))
   c.inc_fit[[groups[i]]]<- quiet(cuminc(ftime = r.hospitalised.death$time,  
              fstatus = r.hospitalised.death$status, 
            group = r.hospitalised.death[[groups[i]]]) )
  
}


# extract estimates at 45 days
tp<-list()
tp[["overall"]]<-timepoints(c.inc_fit[["overall"]],45)$est

for(i in 1:length(groups)){
   tp[[groups[i]]]<- timepoints(c.inc_fit[[groups[i]]],45)$est

}

t1<-data.frame( 
  trans=6,
  group=c("all", rep("Age",6), 
          rep("Sex",2), 
          rep("Charlson",4),
          rep("Autoimmune condition",2),
          rep("Chronic kidney disease",2),
          rep("COPD",2),
          rep("Dementia",2),
          rep("Heart disease",2),
          rep("Hyperlipidemia",2),
          rep("Malignant neoplasm",2),
          rep("Obesity",2),
          rep("Type 2 diabetes",2)
          ),
   level=c("all",
           levels(covid.data$age_gr),
           levels(covid.data$gender),
           levels(covid.data$charlson),
           "0", "1","0", "1","0", "1","0", "1","0", "1","0", "1",
           "0", "1","0", "1","0", "1"),
est=c(
tp$overall[1],
tp$age_gr[1:6],
tp$gender[1:2],
tp$charlson[1:4],
tp$a_autoimmune_condition[1:2],
tp$a_chronic_kidney_disease[1:2],
tp$a_copd[1:2],
tp$a_dementia[1:2],
tp$a_heart_disease[1:2],
tp$a_hyperlipidemia[1:2],
tp$a_malignant_neoplasm[1:2],
tp$a_obesity.5y[1:2],
tp$a_t2_diabetes[1:2]
))


# add to table
events.t6<-events.t6 %>% 
  left_join(t1,
            by = c("trans","group", "level"))
# format number
events.t6<-events.t6 %>% 
  mutate(est=paste0(nice.num2(est*100), "%"))

# pivot wide for consistency in names etc
events.t6.wide<-events.t6 %>% 
  pivot_wider(names_from = trans,
    values_from = c(events, est))

events.t6.wide<-events.t6.wide %>% 
  mutate(events_death.hosp=paste0(events_6, " (", est_6, ")")) %>% 
  select(group, level, n, time, events_death.hosp) %>% 
  rename(n.hosp=n,
        time.hosp=time)
From general population
From diagnosed with COVID-19
From hospitalised with COVID-19
Follow-up in days
To diagnosis with COVID-19
To hospitalised with COVID-19
To death
Follow-up in days
To hospitalised with COVID-19
To death
Follow-up in days
To death
n Median (min, interquartile range, max) Events (cumulative incidence at 67 days) Events (cumulative incidence at 67 days) Events (cumulative incidence at 67 days n Median (min, interquartile range, max) Events (cumulative incidence at 45 days) Events (cumulative incidence at 45 days) n Median (min, interquartile range, max) Events (cumulative incidence at 45 days)
all all 5,627,520 67 (1, 67 to 67, 67) 109,367 (1.94%) 8,582 (0.15%) 11,726 (0.21%) 109,367 34 (0, 18 to 44, 66) 9,437 (9.03%) 2,794 (3.11%) 18,019 36 (0, 23 to 42, 65) 2,791 (19.03%)
Age Under 18 967,227 67 (1, 67 to 67, 67) 4,547 (0.47%) 34 (0.0035%) 9 (0.00093%) 4,547 29 (0, 16 to 40, 65) 40 (0.96%) 0 (0.00%) 74 28 (0, 19 to 41.75, 55) 1 (2.13%)
Age 18 to 39 1,438,732 67 (1, 67 to 67, 67) 30,640 (2.13%) 432 (0.03%) 80 (0.0056%) 30,640 37 (0, 22 to 44, 66) 857 (2.92%) 5 (0.031%) 1,289 36 (0, 27 to 43, 65) 11 (1.14%)
Age 40 to 59 1,788,832 67 (1, 67 to 67, 67) 44,803 (2.51%) 1,815 (0.10%) 658 (0.037%) 44,803 37 (0, 22 to 44, 66) 3,477 (8.08%) 63 (0.18%) 5,292 37 (0, 29 to 43, 65) 143 (3.52%)
Age 60 to 69 617,929 67 (1, 67 to 67, 67) 10,661 (1.73%) 1,590 (0.26%) 1,034 (0.17%) 10,661 34 (0, 14 to 43, 65) 1,708 (16.62%) 136 (1.53%) 3,298 38 (0, 30 to 44, 64) 296 (10.78%)
Age 70 to 79 474,369 67 (1, 67 to 67, 67) 7,163 (1.51%) 2,252 (0.47%) 1,999 (0.42%) 7,163 22 (0, 7 to 39, 65) 1,702 (24.79%) 421 (7.54%) 3,954 37 (0, 24 to 43, 65) 840 (24.71%)
Age 80 or older 340,431 67 (1, 67 to 67, 67) 11,553 (3.40%) 2,459 (0.72%) 7,946 (2.34%) 11,553 19 (0, 9 to 29, 62) 1,653 (15.49%) 2,169 (29.55%) 4,112 25 (0, 14 to 37, 65) 1,500 (47.86%)
Sex Male 2,768,246 67 (1, 67 to 67, 67) 44,894 (1.62%) 4,812 (0.17%) 5,794 (0.21%) 44,894 34 (0, 16 to 44, 66) 5,080 (11.81%) 1,169 (3.19%) 9,892 36 (0, 25 to 43, 65) 1,669 (20.47%)
Sex Female 2,859,274 67 (1, 67 to 67, 67) 64,473 (2.26%) 3,770 (0.13%) 5,932 (0.21%) 64,473 34 (0, 19 to 44, 66) 4,357 (7.09%) 1,625 (3.05%) 8,127 35 (0, 23 to 42, 65) 1,122 (17.15%)
Charlson 0 4,572,265 67 (1, 67 to 67, 67) 81,833 (1.79%) 3,750 (0.082%) 1,672 (0.037%) 81,833 36 (0, 21 to 44, 66) 5,513 (7.03%) 391 (0.58%) 9,263 37 (0, 28 to 43, 65) 604 (8.33%)
Charlson 1 410,497 67 (1, 67 to 67, 67) 10,708 (2.61%) 1,156 (0.28%) 1,724 (0.42%) 10,708 29 (0, 15 to 42, 65) 1,157 (11.33%) 566 (6.60%) 2,313 35 (0, 21 to 43, 65) 447 (23.59%)
Charlson 2 363,057 67 (2, 67 to 67, 67) 8,211 (2.26%) 1,518 (0.42%) 2,398 (0.66%) 8,211 27 (0, 12 to 41, 65) 1,266 (16.16%) 507 (7.95%) 2,784 35 (0, 22 to 42, 65) 572 (24.67%)
Charlson 3+ 281,701 67 (1, 67 to 67, 67) 8,615 (3.06%) 2,158 (0.77%) 5,932 (2.11%) 8,615 20 (0, 9 to 33, 64) 1,501 (18.56%) 1,330 (21.49%) 3,659 29 (0, 16 to 40, 65) 1,168 (40.08%)
Autoimmune condition 0 5,350,070 67 (1, 67 to 67, 67) 102,431 (1.92%) 7,711 (0.14%) 10,406 (0.19%) 102,431 34 (0, 18 to 44, 66) 8,651 (8.84%) 2,503 (2.98%) 16,362 36 (0, 24 to 43, 65) 2,451 (18.45%)
Autoimmune condition 1 277,450 67 (2, 67 to 67, 67) 6,936 (2.50%) 871 (0.31%) 1,320 (0.48%) 6,936 32 (0, 15 to 43, 64) 786 (11.82%) 291 (4.98%) 1,657 34 (0, 21 to 42, 65) 340 (24.74%)
Chronic kidney disease 0 5,415,678 67 (1, 67 to 67, 67) 103,524 (1.91%) 7,034 (0.13%) 7,882 (0.15%) 103,524 35 (0, 19 to 44, 66) 8,379 (8.46%) 1,839 (2.17%) 15,413 36 (0, 25 to 43, 65) 1,933 (15.44%)
Chronic kidney disease 1 211,842 67 (1, 67 to 67, 67) 5,843 (2.76%) 1,548 (0.73%) 3,844 (1.82%) 5,843 19 (0, 9 to 33, 62) 1,058 (19.26%) 955 (22.31%) 2,606 29 (0, 16 to 40, 64) 858 (41.16%)
COPD 0 5,504,775 67 (1, 67 to 67, 67) 106,478 (1.94%) 7,781 (0.14%) 10,238 (0.19%) 106,478 34 (0, 19 to 44, 66) 8,922 (8.76%) 2,514 (2.87%) 16,703 36 (0, 24 to 43, 65) 2,412 (17.75%)
COPD 1 122,745 67 (2, 67 to 67, 67) 2,889 (2.35%) 801 (0.65%) 1,488 (1.21%) 2,889 22 (0, 9 to 37, 65) 515 (18.85%) 280 (12.75%) 1,316 31 (0, 18 to 42, 65) 379 (35.42%)
Dementia 0 5,570,210 67 (1, 67 to 67, 67) 104,572 (1.88%) 7,984 (0.14%) 8,832 (0.16%) 104,572 35 (0, 19 to 44, 66) 8,951 (8.94%) 1,686 (1.95%) 16,935 36 (0, 25 to 43, 65) 2,375 (17.24%)
Dementia 1 57,310 67 (1, 67 to 67, 67) 4,795 (8.37%) 598 (1.04%) 2,894 (5.05%) 4,795 19 (0, 11 to 27, 59) 486 (11.11%) 1,108 (40.04%) 1,084 21 (0, 12 to 31, 65) 416 (54.96%)
Heart disease 0 5,079,725 67 (1, 67 to 67, 67) 95,827 (1.89%) 5,736 (0.11%) 5,861 (0.12%) 95,827 35 (0, 19 to 44, 66) 7,305 (7.97%) 1,530 (1.93%) 13,041 36 (0, 26 to 43, 65) 1,420 (13.67%)
Heart disease 1 547,795 67 (1, 67 to 67, 67) 13,540 (2.47%) 2,846 (0.52%) 5,865 (1.07%) 13,540 23 (0, 11 to 40, 65) 2,132 (16.55%) 1,264 (12.07%) 4,978 32 (0, 18 to 41, 65) 1,371 (33.29%)
Hyperlipidemia 0 5,106,683 67 (1, 67 to 67, 67) 97,479 (1.91%) 6,992 (0.14%) 10,096 (0.20%) 97,479 34 (0, 19 to 44, 66) 7,838 (8.41%) 2,379 (2.97%) 14,830 35 (0, 23 to 42, 65) 2,268 (18.75%)
Hyperlipidemia 1 520,837 67 (1, 67 to 67, 67) 11,888 (2.28%) 1,590 (0.31%) 1,630 (0.31%) 11,888 30 (0, 14 to 43, 65) 1,599 (14.04%) 415 (4.27%) 3,189 36 (0, 25 to 43, 65) 523 (20.21%)
Malignant neoplasm 0 5,330,111 67 (1, 67 to 67, 67) 102,711 (1.93%) 7,177 (0.13%) 7,908 (0.15%) 102,711 34 (0, 19 to 44, 66) 8,362 (8.52%) 2,168 (2.55%) 15,539 36 (0, 24 to 43, 65) 2,100 (16.62%)
Malignant neoplasm 1 297,409 67 (1, 67 to 67, 67) 6,656 (2.24%) 1,405 (0.47%) 3,818 (1.28%) 6,656 26.5 (0, 11 to 41, 65) 1,075 (16.86%) 626 (12.00%) 2,480 33 (0, 19 to 42, 65) 691 (33.72%)
Obesity 0 4,689,927 67 (1, 67 to 67, 67) 86,874 (1.85%) 5,198 (0.11%) 8,475 (0.18%) 86,874 35 (0, 19 to 44, 66) 6,151 (7.42%) 2,061 (2.90%) 11,349 35 (0, 23 to 42, 65) 1,673 (18.51%)
Obesity 1 937,593 67 (1, 67 to 67, 67) 22,493 (2.40%) 3,384 (0.36%) 3,251 (0.35%) 22,493 30 (0, 14 to 42, 66) 3,286 (15.22%) 733 (3.92%) 6,670 36 (0, 24 to 43, 65) 1,118 (19.88%)
Type 2 diabetes 0 5,302,576 67 (1, 67 to 67, 67) 101,940 (1.92%) 6,809 (0.13%) 9,042 (0.17%) 101,940 35 (0, 19 to 44, 66) 8,015 (8.22%) 2,157 (2.58%) 14,824 36 (0, 24 to 43, 65) 2,057 (17.15%)
Type 2 diabetes 1 324,944 67 (2, 67 to 67, 67) 7,427 (2.29%) 1,773 (0.55%) 2,684 (0.83%) 7,427 22 (0, 9 to 38, 64) 1,422 (20.10%) 637 (10.93%) 3,195 34 (0, 21 to 42, 65) 734 (27.80%)

Transition 1: From general population to diagnosed with COVID-19

overall

Date Number at risk Cumulative events Cumulative censored
2020-02-29 5,627,520 0 0
2020-03-05 5,626,131 184 1,522
2020-03-10 5,624,299 976 2,788
2020-03-15 5,618,990 4,372 4,855
2020-03-20 5,601,568 23,202 7,389
2020-03-25 5,581,168 41,194 10,065
2020-03-30 5,564,967 55,519 12,467
2020-04-04 5,545,976 67,934 15,044
2020-04-09 5,534,483 78,825 17,145
2020-04-14 5,526,416 86,066 18,149
2020-04-19 5,514,426 93,399 20,393
2020-04-24 5,505,711 100,897 22,527
2020-04-29 5,500,060 105,153 24,223
2020-05-04 5,495,815 107,765 25,280

By gender

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 gender=Male 2,768,246 0 0
2020-02-29 gender=Female 2,859,274 0 0
2020-03-05 gender=Male 2,767,555 62 796
2020-03-05 gender=Female 2,858,576 122 726
2020-03-10 gender=Male 2,766,634 402 1,467
2020-03-10 gender=Female 2,857,665 574 1,321
2020-03-15 gender=Male 2,764,197 1,826 2,542
2020-03-15 gender=Female 2,854,793 2,546 2,313
2020-03-20 gender=Male 2,756,157 10,237 3,944
2020-03-20 gender=Female 2,845,411 12,965 3,445
2020-03-25 gender=Male 2,746,832 18,211 5,464
2020-03-25 gender=Female 2,834,336 22,983 4,601
2020-03-30 gender=Male 2,739,460 24,305 6,833
2020-03-30 gender=Female 2,825,507 31,214 5,634
2020-04-04 gender=Male 2,731,513 29,111 8,214
2020-04-04 gender=Female 2,814,463 38,823 6,830
2020-04-09 gender=Male 2,726,839 33,314 9,298
2020-04-09 gender=Female 2,807,644 45,511 7,847
2020-04-14 gender=Male 2,723,689 36,056 9,766
2020-04-14 gender=Female 2,802,727 50,010 8,383
2020-04-19 gender=Male 2,718,947 38,699 10,856
2020-04-19 gender=Female 2,795,479 54,700 9,537
2020-04-24 gender=Male 2,715,537 41,528 11,886
2020-04-24 gender=Female 2,790,174 59,369 10,641
2020-04-29 gender=Male 2,713,248 43,138 12,704
2020-04-29 gender=Female 2,786,812 62,015 11,519
2020-05-04 gender=Male 2,711,351 44,268 13,247
2020-05-04 gender=Female 2,784,464 63,497 12,033

By age

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 age_gr=Under 18 967,227 0 0
2020-02-29 age_gr=18 to 39 1,438,732 0 0
2020-02-29 age_gr=40 to 59 1,788,832 0 0
2020-02-29 age_gr=60 to 69 617,929 0 0
2020-02-29 age_gr=70 to 79 474,369 0 0
2020-02-29 age_gr=80 or older 340,431 0 0
2020-03-05 age_gr=Under 18 967,148 3 96
2020-03-05 age_gr=18 to 39 1,438,396 51 396
2020-03-05 age_gr=40 to 59 1,788,516 89 295
2020-03-05 age_gr=60 to 69 617,797 30 140
2020-03-05 age_gr=70 to 79 474,235 7 151
2020-03-05 age_gr=80 or older 340,039 4 444
2020-03-10 age_gr=Under 18 967,057 13 170
2020-03-10 age_gr=18 to 39 1,437,927 235 724
2020-03-10 age_gr=40 to 59 1,788,016 468 556
2020-03-10 age_gr=60 to 69 617,593 139 259
2020-03-10 age_gr=70 to 79 474,071 63 279
2020-03-10 age_gr=80 or older 339,635 58 800
2020-03-15 age_gr=Under 18 966,925 41 273
2020-03-15 age_gr=18 to 39 1,436,495 1,265 1,135
2020-03-15 age_gr=40 to 59 1,786,021 2,131 964
2020-03-15 age_gr=60 to 69 617,051 487 476
2020-03-15 age_gr=70 to 79 473,621 263 581
2020-03-15 age_gr=80 or older 338,877 185 1,426
2020-03-20 age_gr=Under 18 966,550 510 299
2020-03-20 age_gr=18 to 39 1,431,489 7,071 1,402
2020-03-20 age_gr=40 to 59 1,778,104 11,222 1,404
2020-03-20 age_gr=60 to 69 615,231 2,424 850
2020-03-20 age_gr=70 to 79 472,402 1,217 1,159
2020-03-20 age_gr=80 or older 337,792 758 2,275
2020-03-25 age_gr=Under 18 966,050 993 332
2020-03-25 age_gr=18 to 39 1,426,062 12,307 1,609
2020-03-25 age_gr=40 to 59 1,769,519 19,296 1,907
2020-03-25 age_gr=60 to 69 612,789 4,398 1,317
2020-03-25 age_gr=70 to 79 470,557 2,479 1,803
2020-03-25 age_gr=80 or older 336,191 1,721 3,097
2020-03-30 age_gr=Under 18 965,564 1,457 352
2020-03-30 age_gr=18 to 39 1,422,131 16,227 1,794
2020-03-30 age_gr=40 to 59 1,763,306 25,497 2,376
2020-03-30 age_gr=60 to 69 610,854 5,950 1,714
2020-03-30 age_gr=70 to 79 468,883 3,469 2,383
2020-03-30 age_gr=80 or older 334,229 2,919 3,848
2020-04-04 age_gr=Under 18 964,820 2,048 377
2020-04-04 age_gr=18 to 39 1,417,358 19,687 1,970
2020-04-04 age_gr=40 to 59 1,756,123 30,302 2,802
2020-04-04 age_gr=60 to 69 608,859 7,167 2,049
2020-04-04 age_gr=70 to 79 467,358 4,334 2,857
2020-04-04 age_gr=80 or older 331,458 4,396 4,989
2020-04-09 age_gr=Under 18 964,348 2,634 398
2020-04-09 age_gr=18 to 39 1,414,606 22,613 2,142
2020-04-09 age_gr=40 to 59 1,752,126 34,418 3,157
2020-04-09 age_gr=60 to 69 607,762 8,154 2,289
2020-04-09 age_gr=70 to 79 466,393 5,023 3,211
2020-04-09 age_gr=80 or older 329,248 5,983 5,948
2020-04-14 age_gr=Under 18 963,998 2,977 410
2020-04-14 age_gr=18 to 39 1,412,804 24,489 2,211
2020-04-14 age_gr=40 to 59 1,749,659 36,961 3,325
2020-04-14 age_gr=60 to 69 607,070 8,739 2,392
2020-04-14 age_gr=70 to 79 465,724 5,495 3,378
2020-04-14 age_gr=80 or older 327,161 7,405 6,433
2020-04-19 age_gr=Under 18 963,456 3,338 459
2020-04-19 age_gr=18 to 39 1,410,279 26,193 2,388
2020-04-19 age_gr=40 to 59 1,746,184 39,227 3,582
2020-04-19 age_gr=60 to 69 606,044 9,358 2,578
2020-04-19 age_gr=70 to 79 464,575 6,119 3,740
2020-04-19 age_gr=80 or older 323,888 9,164 7,646
2020-04-24 age_gr=Under 18 963,003 3,811 494
2020-04-24 age_gr=18 to 39 1,408,187 28,282 2,635
2020-04-24 age_gr=40 to 59 1,743,638 41,798 3,877
2020-04-24 age_gr=60 to 69 605,380 9,959 2,759
2020-04-24 age_gr=70 to 79 463,869 6,623 4,026
2020-04-24 age_gr=80 or older 321,634 10,424 8,736
2020-04-29 age_gr=Under 18 962,652 4,141 531
2020-04-29 age_gr=18 to 39 1,406,826 29,441 2,817
2020-04-29 age_gr=40 to 59 1,741,926 43,290 4,106
2020-04-29 age_gr=60 to 69 604,888 10,296 2,919
2020-04-29 age_gr=70 to 79 463,397 6,879 4,284
2020-04-29 age_gr=80 or older 320,371 11,106 9,566
2020-05-04 age_gr=Under 18 962,391 4,374 565
2020-05-04 age_gr=18 to 39 1,405,894 30,197 2,986
2020-05-04 age_gr=40 to 59 1,740,729 44,253 4,299
2020-05-04 age_gr=60 to 69 604,521 10,527 3,002
2020-05-04 age_gr=70 to 79 463,006 7,035 4,421
2020-05-04 age_gr=80 or older 319,274 11,379 10,007

By charlson

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 charlson=0 4,572,265 0 0
2020-02-29 charlson=1 410,497 0 0
2020-02-29 charlson=2 363,057 0 0
2020-02-29 charlson=3+ 281,701 0 0
2020-03-05 charlson=0 4,571,509 152 814
2020-03-05 charlson=1 410,364 13 149
2020-03-05 charlson=2 362,897 15 174
2020-03-05 charlson=3+ 281,361 4 385
2020-03-10 charlson=0 4,570,392 735 1,526
2020-03-10 charlson=1 410,196 102 251
2020-03-10 charlson=2 362,693 74 328
2020-03-10 charlson=3+ 281,018 65 683
2020-03-15 charlson=0 4,566,799 3,432 2,525
2020-03-15 charlson=1 409,735 383 460
2020-03-15 charlson=2 362,184 339 590
2020-03-15 charlson=3+ 280,272 218 1,280
2020-03-20 charlson=0 4,553,390 18,712 3,615
2020-03-20 charlson=1 408,137 2,029 741
2020-03-20 charlson=2 360,932 1,508 988
2020-03-20 charlson=3+ 279,109 953 2,045
2020-03-25 charlson=0 4,538,019 32,956 4,784
2020-03-25 charlson=1 406,294 3,599 1,089
2020-03-25 charlson=2 359,276 2,739 1,449
2020-03-25 charlson=3+ 277,579 1,900 2,743
2020-03-30 charlson=0 4,526,633 43,860 5,811
2020-03-30 charlson=1 404,704 4,901 1,380
2020-03-30 charlson=2 357,781 3,825 1,868
2020-03-30 charlson=3+ 275,849 2,933 3,408
2020-04-04 charlson=0 4,513,349 52,984 6,732
2020-04-04 charlson=1 402,809 6,130 1,713
2020-04-04 charlson=2 356,155 4,772 2,300
2020-04-04 charlson=3+ 273,663 4,048 4,299
2020-04-09 charlson=0 4,505,766 60,824 7,468
2020-04-09 charlson=1 401,582 7,237 2,008
2020-04-09 charlson=2 355,075 5,635 2,675
2020-04-09 charlson=3+ 272,060 5,129 4,994
2020-04-14 charlson=0 4,500,816 65,755 7,796
2020-04-14 charlson=1 400,626 8,074 2,146
2020-04-14 charlson=2 354,220 6,251 2,856
2020-04-14 charlson=3+ 270,754 5,986 5,351
2020-04-19 charlson=0 4,493,753 70,396 8,483
2020-04-19 charlson=1 399,158 8,990 2,437
2020-04-19 charlson=2 352,951 6,961 3,235
2020-04-19 charlson=3+ 268,564 7,052 6,238
2020-04-24 charlson=0 4,488,361 75,669 9,228
2020-04-24 charlson=1 398,135 9,812 2,724
2020-04-24 charlson=2 352,068 7,564 3,596
2020-04-24 charlson=3+ 267,147 7,852 6,979
2020-04-29 charlson=0 4,484,759 78,705 9,805
2020-04-29 charlson=1 397,529 10,263 2,952
2020-04-29 charlson=2 351,520 7,904 3,880
2020-04-29 charlson=3+ 266,252 8,281 7,586
2020-05-04 charlson=0 4,482,261 80,660 10,272
2020-05-04 charlson=1 397,044 10,534 3,052
2020-05-04 charlson=2 351,026 8,096 4,049
2020-05-04 charlson=3+ 265,484 8,475 7,907

By a_autoimmune_condition

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_autoimmune_condition=0 5,350,070 0 0
2020-02-29 a_autoimmune_condition=1 277,450 0 0
2020-03-05 a_autoimmune_condition=0 5,348,777 172 1,414
2020-03-05 a_autoimmune_condition=1 277,354 12 108
2020-03-10 a_autoimmune_condition=0 5,347,098 909 2,574
2020-03-10 a_autoimmune_condition=1 277,201 67 214
2020-03-15 a_autoimmune_condition=0 5,342,174 4,087 4,455
2020-03-15 a_autoimmune_condition=1 276,816 285 400
2020-03-20 a_autoimmune_condition=0 5,325,892 21,764 6,755
2020-03-20 a_autoimmune_condition=1 275,676 1,438 634
2020-03-25 a_autoimmune_condition=0 5,306,825 38,662 9,185
2020-03-25 a_autoimmune_condition=1 274,343 2,532 880
2020-03-30 a_autoimmune_condition=0 5,291,709 52,083 11,362
2020-03-30 a_autoimmune_condition=1 273,258 3,436 1,105
2020-04-04 a_autoimmune_condition=0 5,274,026 63,692 13,691
2020-04-04 a_autoimmune_condition=1 271,950 4,242 1,353
2020-04-09 a_autoimmune_condition=0 5,263,311 73,886 15,577
2020-04-09 a_autoimmune_condition=1 271,172 4,939 1,568
2020-04-14 a_autoimmune_condition=0 5,255,822 80,652 16,481
2020-04-14 a_autoimmune_condition=1 270,594 5,414 1,668
2020-04-19 a_autoimmune_condition=0 5,244,775 87,431 18,510
2020-04-19 a_autoimmune_condition=1 269,651 5,968 1,883
2020-04-24 a_autoimmune_condition=0 5,236,645 94,482 20,458
2020-04-24 a_autoimmune_condition=1 269,066 6,415 2,069
2020-04-29 a_autoimmune_condition=0 5,231,350 98,489 22,004
2020-04-29 a_autoimmune_condition=1 268,710 6,664 2,219
2020-05-04 a_autoimmune_condition=0 5,227,423 100,942 22,966
2020-05-04 a_autoimmune_condition=1 268,392 6,823 2,314

By a_chronic_kidney_disease

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_chronic_kidney_disease=0 5,415,678 0 0
2020-02-29 a_chronic_kidney_disease=1 211,842 0 0
2020-03-05 a_chronic_kidney_disease=0 5,414,480 183 1,298
2020-03-05 a_chronic_kidney_disease=1 211,651 1 224
2020-03-10 a_chronic_kidney_disease=0 5,412,861 946 2,378
2020-03-10 a_chronic_kidney_disease=1 211,438 30 410
2020-03-15 a_chronic_kidney_disease=0 5,407,958 4,240 4,127
2020-03-15 a_chronic_kidney_disease=1 211,032 132 728
2020-03-20 a_chronic_kidney_disease=0 5,391,344 22,576 6,122
2020-03-20 a_chronic_kidney_disease=1 210,224 626 1,267
2020-03-25 a_chronic_kidney_disease=0 5,372,036 39,879 8,326
2020-03-25 a_chronic_kidney_disease=1 209,132 1,315 1,739
2020-03-30 a_chronic_kidney_disease=0 5,357,039 53,475 10,248
2020-03-30 a_chronic_kidney_disease=1 207,928 2,044 2,219
2020-04-04 a_chronic_kidney_disease=0 5,339,574 65,132 12,201
2020-04-04 a_chronic_kidney_disease=1 206,402 2,802 2,843
2020-04-09 a_chronic_kidney_disease=0 5,329,185 75,264 13,801
2020-04-09 a_chronic_kidney_disease=1 205,298 3,561 3,344
2020-04-14 a_chronic_kidney_disease=0 5,322,027 81,942 14,563
2020-04-14 a_chronic_kidney_disease=1 204,389 4,124 3,586
2020-04-19 a_chronic_kidney_disease=0 5,311,447 88,577 16,243
2020-04-19 a_chronic_kidney_disease=1 202,979 4,822 4,150
2020-04-24 a_chronic_kidney_disease=0 5,303,686 95,550 17,873
2020-04-24 a_chronic_kidney_disease=1 202,025 5,347 4,654
2020-04-29 a_chronic_kidney_disease=0 5,298,621 99,514 19,181
2020-04-29 a_chronic_kidney_disease=1 201,439 5,639 5,042
2020-05-04 a_chronic_kidney_disease=0 5,294,888 102,007 20,016
2020-05-04 a_chronic_kidney_disease=1 200,927 5,758 5,264

By a_copd

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_copd=0 5,504,775 0 0
2020-02-29 a_copd=1 122,745 0 0
2020-03-05 a_copd=0 5,503,475 181 1,419
2020-03-05 a_copd=1 122,656 3 103
2020-03-10 a_copd=0 5,501,747 946 2,604
2020-03-10 a_copd=1 122,552 30 184
2020-03-15 a_copd=0 5,496,698 4,274 4,475
2020-03-15 a_copd=1 122,292 98 380
2020-03-20 a_copd=0 5,479,797 22,732 6,757
2020-03-20 a_copd=1 121,771 470 632
2020-03-25 a_copd=0 5,459,959 40,346 9,210
2020-03-25 a_copd=1 121,209 848 855
2020-03-30 a_copd=0 5,444,340 54,321 11,398
2020-03-30 a_copd=1 120,627 1,198 1,069
2020-04-04 a_copd=0 5,426,001 66,389 13,727
2020-04-04 a_copd=1 119,975 1,545 1,317
2020-04-09 a_copd=0 5,414,970 76,982 15,626
2020-04-09 a_copd=1 119,513 1,843 1,519
2020-04-14 a_copd=0 5,407,253 83,980 16,534
2020-04-14 a_copd=1 119,163 2,086 1,615
2020-04-19 a_copd=0 5,395,840 91,026 18,560
2020-04-19 a_copd=1 118,586 2,373 1,833
2020-04-24 a_copd=0 5,387,537 98,272 20,507
2020-04-24 a_copd=1 118,174 2,625 2,020
2020-04-29 a_copd=0 5,382,126 102,404 22,044
2020-04-29 a_copd=1 117,934 2,749 2,179
2020-05-04 a_copd=0 5,378,108 104,949 23,001
2020-05-04 a_copd=1 117,707 2,816 2,279

By a_dementia

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_dementia=0 5,570,210 0 0
2020-02-29 a_dementia=1 57,310 0 0
2020-03-05 a_dementia=0 5,568,939 184 1,390
2020-03-05 a_dementia=1 57,192 0 132
2020-03-10 a_dementia=0 5,567,221 963 2,551
2020-03-10 a_dementia=1 57,078 13 237
2020-03-15 a_dementia=0 5,562,101 4,341 4,453
2020-03-15 a_dementia=1 56,889 31 402
2020-03-20 a_dementia=0 5,544,920 23,041 6,779
2020-03-20 a_dementia=1 56,648 161 610
2020-03-25 a_dementia=0 5,524,879 40,777 9,290
2020-03-25 a_dementia=1 56,289 417 775
2020-03-30 a_dementia=0 5,509,252 54,676 11,522
2020-03-30 a_dementia=1 55,715 843 945
2020-04-04 a_dementia=0 5,491,324 66,446 13,741
2020-04-04 a_dementia=1 54,652 1,488 1,303
2020-04-09 a_dementia=0 5,480,691 76,640 15,503
2020-04-09 a_dementia=1 53,792 2,185 1,642
2020-04-14 a_dementia=0 5,473,567 83,174 16,316
2020-04-14 a_dementia=1 52,849 2,892 1,833
2020-04-19 a_dementia=0 5,463,121 89,610 18,066
2020-04-19 a_dementia=1 51,305 3,789 2,327
2020-04-24 a_dementia=0 5,455,345 96,578 19,763
2020-04-24 a_dementia=1 50,366 4,319 2,764
2020-04-29 a_dementia=0 5,450,193 100,547 21,145
2020-04-29 a_dementia=1 49,867 4,606 3,078
2020-05-04 a_dementia=0 5,446,370 103,043 22,042
2020-05-04 a_dementia=1 49,445 4,722 3,238

By a_heart_disease

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_heart_disease=0 5,079,725 0 0
2020-02-29 a_heart_disease=1 547,795 0 0
2020-03-05 a_heart_disease=0 5,078,692 173 1,117
2020-03-05 a_heart_disease=1 547,439 11 405
2020-03-10 a_heart_disease=0 5,077,250 858 2,073
2020-03-10 a_heart_disease=1 547,049 118 715
2020-03-15 a_heart_disease=0 5,072,893 3,908 3,504
2020-03-15 a_heart_disease=1 546,097 464 1,351
2020-03-20 a_heart_disease=0 5,057,540 21,043 5,177
2020-03-20 a_heart_disease=1 544,028 2,159 2,212
2020-03-25 a_heart_disease=0 5,039,789 37,170 6,956
2020-03-25 a_heart_disease=1 541,379 4,024 3,109
2020-03-30 a_heart_disease=0 5,026,102 49,787 8,563
2020-03-30 a_heart_disease=1 538,865 5,732 3,904
2020-04-04 a_heart_disease=0 5,010,132 60,518 10,157
2020-04-04 a_heart_disease=1 535,844 7,416 4,887
2020-04-09 a_heart_disease=0 5,000,662 69,928 11,474
2020-04-09 a_heart_disease=1 533,821 8,897 5,671
2020-04-14 a_heart_disease=0 4,994,212 76,056 12,082
2020-04-14 a_heart_disease=1 532,204 10,010 6,067
2020-04-19 a_heart_disease=0 4,984,742 82,069 13,439
2020-04-19 a_heart_disease=1 529,684 11,330 6,954
2020-04-24 a_heart_disease=0 4,977,727 88,469 14,820
2020-04-24 a_heart_disease=1 527,984 12,428 7,707
2020-04-29 a_heart_disease=0 4,973,141 92,108 15,894
2020-04-29 a_heart_disease=1 526,919 13,045 8,329
2020-05-04 a_heart_disease=0 4,969,819 94,434 16,611
2020-05-04 a_heart_disease=1 525,996 13,331 8,669

By a_hyperlipidemia

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_hyperlipidemia=0 5,106,683 0 0
2020-02-29 a_hyperlipidemia=1 520,837 0 0
2020-03-05 a_hyperlipidemia=0 5,105,416 171 1,387
2020-03-05 a_hyperlipidemia=1 520,715 13 135
2020-03-10 a_hyperlipidemia=0 5,103,770 878 2,532
2020-03-10 a_hyperlipidemia=1 520,529 98 256
2020-03-15 a_hyperlipidemia=0 5,098,984 3,925 4,373
2020-03-15 a_hyperlipidemia=1 520,006 447 482
2020-03-20 a_hyperlipidemia=0 5,083,527 20,762 6,504
2020-03-20 a_hyperlipidemia=1 518,041 2,440 885
2020-03-25 a_hyperlipidemia=0 5,065,469 36,833 8,708
2020-03-25 a_hyperlipidemia=1 515,699 4,361 1,357
2020-03-30 a_hyperlipidemia=0 5,051,255 49,540 10,693
2020-03-30 a_hyperlipidemia=1 513,712 5,979 1,774
2020-04-04 a_hyperlipidemia=0 5,034,476 60,549 12,923
2020-04-04 a_hyperlipidemia=1 511,500 7,385 2,121
2020-04-09 a_hyperlipidemia=0 5,024,309 70,248 14,726
2020-04-09 a_hyperlipidemia=1 510,174 8,577 2,419
2020-04-14 a_hyperlipidemia=0 5,017,120 76,737 15,593
2020-04-14 a_hyperlipidemia=1 509,296 9,329 2,556
2020-04-19 a_hyperlipidemia=0 5,006,528 83,211 17,554
2020-04-19 a_hyperlipidemia=1 507,898 10,188 2,839
2020-04-24 a_hyperlipidemia=0 4,998,802 89,906 19,422
2020-04-24 a_hyperlipidemia=1 506,909 10,991 3,105
2020-04-29 a_hyperlipidemia=0 4,993,771 93,688 20,907
2020-04-29 a_hyperlipidemia=1 506,289 11,465 3,316
2020-05-04 a_hyperlipidemia=0 4,989,987 96,042 21,850
2020-05-04 a_hyperlipidemia=1 505,828 11,723 3,430

By a_hypertension

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_hypertension=0 4,924,353 0 0
2020-02-29 a_hypertension=1 703,167 0 0
2020-03-05 a_hypertension=0 4,923,233 155 1,236
2020-03-05 a_hypertension=1 702,898 29 286
2020-03-10 a_hypertension=0 4,921,708 817 2,276
2020-03-10 a_hypertension=1 702,591 159 512
2020-03-15 a_hypertension=0 4,917,288 3,730 3,896
2020-03-15 a_hypertension=1 701,702 642 959
2020-03-20 a_hypertension=0 4,902,358 20,171 5,728
2020-03-20 a_hypertension=1 699,210 3,031 1,661
2020-03-25 a_hypertension=0 4,885,164 35,651 7,611
2020-03-25 a_hypertension=1 696,004 5,543 2,454
2020-03-30 a_hypertension=0 4,871,704 47,940 9,304
2020-03-30 a_hypertension=1 693,263 7,579 3,163
2020-04-04 a_hypertension=0 4,855,829 58,503 11,130
2020-04-04 a_hypertension=1 690,147 9,431 3,914
2020-04-09 a_hypertension=0 4,846,380 67,689 12,658
2020-04-09 a_hypertension=1 688,103 11,136 4,487
2020-04-14 a_hypertension=0 4,839,876 73,640 13,384
2020-04-14 a_hypertension=1 686,540 12,426 4,765
2020-04-19 a_hypertension=0 4,830,390 79,483 15,016
2020-04-19 a_hypertension=1 684,036 13,916 5,377
2020-04-24 a_hypertension=0 4,823,371 85,742 16,558
2020-04-24 a_hypertension=1 682,340 15,155 5,969
2020-04-29 a_hypertension=0 4,818,708 89,338 17,821
2020-04-29 a_hypertension=1 681,352 15,815 6,402
2020-05-04 a_hypertension=0 4,815,278 91,554 18,625
2020-05-04 a_hypertension=1 680,537 16,211 6,655

By a_malignant_neoplasm

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_malignant_neoplasm=0 5,330,111 0 0
2020-02-29 a_malignant_neoplasm=1 297,409 0 0
2020-03-05 a_malignant_neoplasm=0 5,328,988 174 1,229
2020-03-05 a_malignant_neoplasm=1 297,143 10 293
2020-03-10 a_malignant_neoplasm=0 5,327,436 912 2,256
2020-03-10 a_malignant_neoplasm=1 296,863 64 532
2020-03-15 a_malignant_neoplasm=0 5,322,764 4,094 3,901
2020-03-15 a_malignant_neoplasm=1 296,226 278 954
2020-03-20 a_malignant_neoplasm=0 5,306,486 21,992 5,909
2020-03-20 a_malignant_neoplasm=1 295,082 1,210 1,480
2020-03-25 a_malignant_neoplasm=0 5,287,532 39,003 8,058
2020-03-25 a_malignant_neoplasm=1 293,636 2,191 2,007
2020-03-30 a_malignant_neoplasm=0 5,272,714 52,470 10,013
2020-03-30 a_malignant_neoplasm=1 292,253 3,049 2,454
2020-04-04 a_malignant_neoplasm=0 5,255,239 64,059 12,038
2020-04-04 a_malignant_neoplasm=1 290,737 3,875 3,006
2020-04-09 a_malignant_neoplasm=0 5,244,805 74,228 13,729
2020-04-09 a_malignant_neoplasm=1 289,678 4,597 3,416
2020-04-14 a_malignant_neoplasm=0 5,237,487 80,977 14,525
2020-04-14 a_malignant_neoplasm=1 288,929 5,089 3,624
2020-04-19 a_malignant_neoplasm=0 5,226,756 87,722 16,257
2020-04-19 a_malignant_neoplasm=1 287,670 5,677 4,136
2020-04-24 a_malignant_neoplasm=0 5,218,863 94,719 17,962
2020-04-24 a_malignant_neoplasm=1 286,848 6,178 4,565
2020-04-29 a_malignant_neoplasm=0 5,213,776 98,711 19,254
2020-04-29 a_malignant_neoplasm=1 286,284 6,442 4,969
2020-05-04 a_malignant_neoplasm=0 5,210,049 101,192 20,111
2020-05-04 a_malignant_neoplasm=1 285,766 6,573 5,169

By a_obesity.5y

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_obesity.5y=0 4,689,927 0 0
2020-02-29 a_obesity.5y=1 937,593 0 0
2020-03-05 a_obesity.5y=0 4,688,791 147 1,256
2020-03-05 a_obesity.5y=1 937,340 37 266
2020-03-10 a_obesity.5y=0 4,687,286 786 2,302
2020-03-10 a_obesity.5y=1 937,013 190 486
2020-03-15 a_obesity.5y=0 4,683,041 3,537 3,879
2020-03-15 a_obesity.5y=1 935,949 835 976
2020-03-20 a_obesity.5y=0 4,669,182 18,753 5,589
2020-03-20 a_obesity.5y=1 932,386 4,449 1,800
2020-03-25 a_obesity.5y=0 4,653,385 33,008 7,328
2020-03-25 a_obesity.5y=1 927,783 8,186 2,737
2020-03-30 a_obesity.5y=0 4,641,111 44,240 8,841
2020-03-30 a_obesity.5y=1 923,856 11,279 3,626
2020-04-04 a_obesity.5y=0 4,626,405 53,983 10,607
2020-04-04 a_obesity.5y=1 919,571 13,951 4,437
2020-04-09 a_obesity.5y=0 4,617,461 62,621 12,123
2020-04-09 a_obesity.5y=1 917,022 16,204 5,022
2020-04-14 a_obesity.5y=0 4,611,157 68,347 12,829
2020-04-14 a_obesity.5y=1 915,259 17,719 5,320
2020-04-19 a_obesity.5y=0 4,601,856 74,129 14,495
2020-04-19 a_obesity.5y=1 912,570 19,270 5,898
2020-04-24 a_obesity.5y=0 4,594,963 80,103 16,156
2020-04-24 a_obesity.5y=1 910,748 20,794 6,371
2020-04-29 a_obesity.5y=0 4,590,483 83,476 17,446
2020-04-29 a_obesity.5y=1 909,577 21,677 6,777
2020-05-04 a_obesity.5y=0 4,587,126 85,588 18,287
2020-05-04 a_obesity.5y=1 908,689 22,177 6,993

By a_t2_diabetes

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_t2_diabetes=0 5,302,576 0 0
2020-02-29 a_t2_diabetes=1 324,944 0 0
2020-03-05 a_t2_diabetes=0 5,301,335 178 1,349
2020-03-05 a_t2_diabetes=1 324,796 6 173
2020-03-10 a_t2_diabetes=0 5,299,700 918 2,461
2020-03-10 a_t2_diabetes=1 324,599 58 327
2020-03-15 a_t2_diabetes=0 5,294,891 4,110 4,201
2020-03-15 a_t2_diabetes=1 324,099 262 654
2020-03-20 a_t2_diabetes=0 5,278,543 22,057 6,288
2020-03-20 a_t2_diabetes=1 323,025 1,145 1,101
2020-03-25 a_t2_diabetes=0 5,259,662 38,935 8,454
2020-03-25 a_t2_diabetes=1 321,506 2,259 1,611
2020-03-30 a_t2_diabetes=0 5,244,936 52,284 10,350
2020-03-30 a_t2_diabetes=1 320,031 3,235 2,117
2020-04-04 a_t2_diabetes=0 5,227,607 63,799 12,418
2020-04-04 a_t2_diabetes=1 318,369 4,135 2,626
2020-04-09 a_t2_diabetes=0 5,217,221 73,888 14,097
2020-04-09 a_t2_diabetes=1 317,262 4,937 3,048
2020-04-14 a_t2_diabetes=0 5,209,983 80,530 14,891
2020-04-14 a_t2_diabetes=1 316,433 5,536 3,258
2020-04-19 a_t2_diabetes=0 5,199,365 87,151 16,685
2020-04-19 a_t2_diabetes=1 315,061 6,248 3,708
2020-04-24 a_t2_diabetes=0 5,191,510 94,056 18,462
2020-04-24 a_t2_diabetes=1 314,201 6,841 4,065
2020-04-29 a_t2_diabetes=0 5,186,384 98,004 19,889
2020-04-29 a_t2_diabetes=1 313,676 7,149 4,334
2020-05-04 a_t2_diabetes=0 5,182,569 100,449 20,797
2020-05-04 a_t2_diabetes=1 313,246 7,316 4,483

Transition 2: From general population to hospitalised with COVID-19

overall

Date Number at risk Cumulative events Cumulative censored
2020-02-29 5,627,520 0 0
2020-03-05 5,626,131 50 1,656
2020-03-10 5,624,299 133 3,631
2020-03-15 5,618,990 480 8,747
2020-03-20 5,601,568 1,865 28,726
2020-03-25 5,581,168 3,664 47,595
2020-03-30 5,564,967 5,248 62,738
2020-04-04 5,545,976 6,262 76,716
2020-04-09 5,534,483 7,124 88,846
2020-04-14 5,526,416 7,647 96,568
2020-04-19 5,514,426 7,996 105,796
2020-04-24 5,505,711 8,276 115,148
2020-04-29 5,500,060 8,426 120,950
2020-05-04 5,495,815 8,552 124,493

By gender

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 gender=Male 2,768,246 0 0
2020-02-29 gender=Female 2,859,274 0 0
2020-03-05 gender=Male 2,767,555 22 836
2020-03-05 gender=Female 2,858,576 28 820
2020-03-10 gender=Male 2,766,634 69 1,800
2020-03-10 gender=Female 2,857,665 64 1,831
2020-03-15 gender=Male 2,764,197 283 4,085
2020-03-15 gender=Female 2,854,793 197 4,662
2020-03-20 gender=Male 2,756,157 1,075 13,106
2020-03-20 gender=Female 2,845,411 790 15,620
2020-03-25 gender=Male 2,746,832 2,131 21,544
2020-03-25 gender=Female 2,834,336 1,533 26,051
2020-03-30 gender=Male 2,739,460 3,075 28,063
2020-03-30 gender=Female 2,825,507 2,173 34,675
2020-04-04 gender=Male 2,731,513 3,646 33,679
2020-04-04 gender=Female 2,814,463 2,616 43,037
2020-04-09 gender=Male 2,726,839 4,081 38,531
2020-04-09 gender=Female 2,807,644 3,043 50,315
2020-04-14 gender=Male 2,723,689 4,338 41,484
2020-04-14 gender=Female 2,802,727 3,309 55,084
2020-04-19 gender=Male 2,718,947 4,516 45,039
2020-04-19 gender=Female 2,795,479 3,480 60,757
2020-04-24 gender=Male 2,715,537 4,660 48,754
2020-04-24 gender=Female 2,790,174 3,616 66,394
2020-04-29 gender=Male 2,713,248 4,734 51,108
2020-04-29 gender=Female 2,786,812 3,692 69,842
2020-05-04 gender=Male 2,711,351 4,799 52,716
2020-05-04 gender=Female 2,784,464 3,753 71,777

By age

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 age_gr=Under 18 967,227 0 0
2020-02-29 age_gr=18 to 39 1,438,732 0 0
2020-02-29 age_gr=40 to 59 1,788,832 0 0
2020-02-29 age_gr=60 to 69 617,929 0 0
2020-02-29 age_gr=70 to 79 474,369 0 0
2020-02-29 age_gr=80 or older 340,431 0 0
2020-03-05 age_gr=Under 18 967,148 0 99
2020-03-05 age_gr=18 to 39 1,438,396 3 444
2020-03-05 age_gr=40 to 59 1,788,516 13 371
2020-03-05 age_gr=60 to 69 617,797 8 162
2020-03-05 age_gr=70 to 79 474,235 11 147
2020-03-05 age_gr=80 or older 340,039 15 433
2020-03-10 age_gr=Under 18 967,057 0 183
2020-03-10 age_gr=18 to 39 1,437,927 7 952
2020-03-10 age_gr=40 to 59 1,788,016 31 993
2020-03-10 age_gr=60 to 69 617,593 21 377
2020-03-10 age_gr=70 to 79 474,071 34 308
2020-03-10 age_gr=80 or older 339,635 40 818
2020-03-15 age_gr=Under 18 966,925 5 309
2020-03-15 age_gr=18 to 39 1,436,495 25 2,375
2020-03-15 age_gr=40 to 59 1,786,021 97 2,998
2020-03-15 age_gr=60 to 69 617,051 91 872
2020-03-15 age_gr=70 to 79 473,621 142 702
2020-03-15 age_gr=80 or older 338,877 120 1,491
2020-03-20 age_gr=Under 18 966,550 8 801
2020-03-20 age_gr=18 to 39 1,431,489 88 8,385
2020-03-20 age_gr=40 to 59 1,778,104 356 12,270
2020-03-20 age_gr=60 to 69 615,231 372 2,902
2020-03-20 age_gr=70 to 79 472,402 585 1,791
2020-03-20 age_gr=80 or older 337,792 456 2,577
2020-03-25 age_gr=Under 18 966,050 15 1,310
2020-03-25 age_gr=18 to 39 1,426,062 178 13,738
2020-03-25 age_gr=40 to 59 1,769,519 724 20,479
2020-03-25 age_gr=60 to 69 612,789 754 4,961
2020-03-25 age_gr=70 to 79 470,557 1,120 3,162
2020-03-25 age_gr=80 or older 336,191 873 3,945
2020-03-30 age_gr=Under 18 965,564 16 1,793
2020-03-30 age_gr=18 to 39 1,422,131 245 17,776
2020-03-30 age_gr=40 to 59 1,763,306 1,083 26,790
2020-03-30 age_gr=60 to 69 610,854 1,068 6,596
2020-03-30 age_gr=70 to 79 468,883 1,596 4,256
2020-03-30 age_gr=80 or older 334,229 1,240 5,527
2020-04-04 age_gr=Under 18 964,820 19 2,406
2020-04-04 age_gr=18 to 39 1,417,358 289 21,368
2020-04-04 age_gr=40 to 59 1,756,123 1,332 31,772
2020-04-04 age_gr=60 to 69 608,859 1,270 7,946
2020-04-04 age_gr=70 to 79 467,358 1,833 5,358
2020-04-04 age_gr=80 or older 331,458 1,519 7,866
2020-04-09 age_gr=Under 18 964,348 22 3,010
2020-04-09 age_gr=18 to 39 1,414,606 340 24,415
2020-04-09 age_gr=40 to 59 1,752,126 1,541 36,034
2020-04-09 age_gr=60 to 69 607,762 1,400 9,043
2020-04-09 age_gr=70 to 79 466,393 1,995 6,239
2020-04-09 age_gr=80 or older 329,248 1,826 10,105
2020-04-14 age_gr=Under 18 963,998 24 3,363
2020-04-14 age_gr=18 to 39 1,412,804 361 26,339
2020-04-14 age_gr=40 to 59 1,749,659 1,641 38,645
2020-04-14 age_gr=60 to 69 607,070 1,471 9,660
2020-04-14 age_gr=70 to 79 465,724 2,089 6,784
2020-04-14 age_gr=80 or older 327,161 2,061 11,777
2020-04-19 age_gr=Under 18 963,456 28 3,769
2020-04-19 age_gr=18 to 39 1,410,279 378 28,203
2020-04-19 age_gr=40 to 59 1,746,184 1,705 41,104
2020-04-19 age_gr=60 to 69 606,044 1,517 10,419
2020-04-19 age_gr=70 to 79 464,575 2,159 7,700
2020-04-19 age_gr=80 or older 323,888 2,209 14,601
2020-04-24 age_gr=Under 18 963,003 30 4,275
2020-04-24 age_gr=18 to 39 1,408,187 405 30,512
2020-04-24 age_gr=40 to 59 1,743,638 1,767 43,908
2020-04-24 age_gr=60 to 69 605,380 1,549 11,169
2020-04-24 age_gr=70 to 79 463,869 2,200 8,449
2020-04-24 age_gr=80 or older 321,634 2,325 16,835
2020-04-29 age_gr=Under 18 962,652 32 4,640
2020-04-29 age_gr=18 to 39 1,406,826 417 31,841
2020-04-29 age_gr=40 to 59 1,741,926 1,787 45,609
2020-04-29 age_gr=60 to 69 604,888 1,568 11,647
2020-04-29 age_gr=70 to 79 463,397 2,231 8,932
2020-04-29 age_gr=80 or older 320,371 2,391 18,281
2020-05-04 age_gr=Under 18 962,391 34 4,905
2020-05-04 age_gr=18 to 39 1,405,894 428 32,755
2020-05-04 age_gr=40 to 59 1,740,729 1,811 46,741
2020-05-04 age_gr=60 to 69 604,521 1,586 11,943
2020-05-04 age_gr=70 to 79 463,006 2,247 9,209
2020-05-04 age_gr=80 or older 319,274 2,446 18,940

By charlson

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 charlson=0 4,572,265 0 0
2020-02-29 charlson=1 410,497 0 0
2020-02-29 charlson=2 363,057 0 0
2020-02-29 charlson=3+ 281,701 0 0
2020-03-05 charlson=0 4,571,509 19 947
2020-03-05 charlson=1 410,364 9 153
2020-03-05 charlson=2 362,897 8 181
2020-03-05 charlson=3+ 281,361 14 375
2020-03-10 charlson=0 4,570,392 44 2,217
2020-03-10 charlson=1 410,196 21 332
2020-03-10 charlson=2 362,693 29 373
2020-03-10 charlson=3+ 281,018 39 709
2020-03-15 charlson=0 4,566,799 174 5,783
2020-03-15 charlson=1 409,735 87 756
2020-03-15 charlson=2 362,184 85 844
2020-03-15 charlson=3+ 280,272 134 1,364
2020-03-20 charlson=0 4,553,390 804 21,523
2020-03-20 charlson=1 408,137 256 2,514
2020-03-20 charlson=2 360,932 316 2,180
2020-03-20 charlson=3+ 279,109 489 2,509
2020-03-25 charlson=0 4,538,019 1,658 36,082
2020-03-25 charlson=1 406,294 500 4,188
2020-03-25 charlson=2 359,276 639 3,549
2020-03-25 charlson=3+ 277,579 867 3,776
2020-03-30 charlson=0 4,526,633 2,406 47,265
2020-03-30 charlson=1 404,704 698 5,583
2020-03-30 charlson=2 357,781 919 4,774
2020-03-30 charlson=3+ 275,849 1,225 5,116
2020-04-04 charlson=0 4,513,349 2,881 56,835
2020-04-04 charlson=1 402,809 826 7,017
2020-04-04 charlson=2 356,155 1,086 5,986
2020-04-04 charlson=3+ 273,663 1,469 6,878
2020-04-09 charlson=0 4,505,766 3,231 65,061
2020-04-09 charlson=1 401,582 958 8,287
2020-04-09 charlson=2 355,075 1,243 7,067
2020-04-09 charlson=3+ 272,060 1,692 8,431
2020-04-14 charlson=0 4,500,816 3,407 70,144
2020-04-14 charlson=1 400,626 1,028 9,192
2020-04-14 charlson=2 354,220 1,343 7,764
2020-04-14 charlson=3+ 270,754 1,869 9,468
2020-04-19 charlson=0 4,493,753 3,525 75,354
2020-04-19 charlson=1 399,158 1,073 10,354
2020-04-19 charlson=2 352,951 1,412 8,784
2020-04-19 charlson=3+ 268,564 1,986 11,304
2020-04-24 charlson=0 4,488,361 3,641 81,256
2020-04-24 charlson=1 398,135 1,111 11,425
2020-04-24 charlson=2 352,068 1,461 9,699
2020-04-24 charlson=3+ 267,147 2,063 12,768
2020-04-29 charlson=0 4,484,759 3,688 84,822
2020-04-29 charlson=1 397,529 1,134 12,081
2020-04-29 charlson=2 351,520 1,492 10,292
2020-04-29 charlson=3+ 266,252 2,112 13,755
2020-05-04 charlson=0 4,482,261 3,740 87,192
2020-05-04 charlson=1 397,044 1,153 12,433
2020-05-04 charlson=2 351,026 1,512 10,633
2020-05-04 charlson=3+ 265,484 2,147 14,235

By a_autoimmune_condition

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_autoimmune_condition=0 5,350,070 0 0
2020-02-29 a_autoimmune_condition=1 277,450 0 0
2020-03-05 a_autoimmune_condition=0 5,348,777 42 1,544
2020-03-05 a_autoimmune_condition=1 277,354 8 112
2020-03-10 a_autoimmune_condition=0 5,347,098 116 3,367
2020-03-10 a_autoimmune_condition=1 277,201 17 264
2020-03-15 a_autoimmune_condition=0 5,342,174 425 8,117
2020-03-15 a_autoimmune_condition=1 276,816 55 630
2020-03-20 a_autoimmune_condition=0 5,325,892 1,665 26,854
2020-03-20 a_autoimmune_condition=1 275,676 200 1,872
2020-03-25 a_autoimmune_condition=0 5,306,825 3,287 44,560
2020-03-25 a_autoimmune_condition=1 274,343 377 3,035
2020-03-30 a_autoimmune_condition=0 5,291,709 4,733 58,712
2020-03-30 a_autoimmune_condition=1 273,258 515 4,026
2020-04-04 a_autoimmune_condition=0 5,274,026 5,640 71,743
2020-04-04 a_autoimmune_condition=1 271,950 622 4,973
2020-04-09 a_autoimmune_condition=0 5,263,311 6,413 83,050
2020-04-09 a_autoimmune_condition=1 271,172 711 5,796
2020-04-14 a_autoimmune_condition=0 5,255,822 6,882 90,251
2020-04-14 a_autoimmune_condition=1 270,594 765 6,317
2020-04-19 a_autoimmune_condition=0 5,244,775 7,192 98,749
2020-04-19 a_autoimmune_condition=1 269,651 804 7,047
2020-04-24 a_autoimmune_condition=0 5,236,645 7,432 107,508
2020-04-24 a_autoimmune_condition=1 269,066 844 7,640
2020-04-29 a_autoimmune_condition=0 5,231,350 7,568 112,925
2020-04-29 a_autoimmune_condition=1 268,710 858 8,025
2020-05-04 a_autoimmune_condition=0 5,227,423 7,684 116,224
2020-05-04 a_autoimmune_condition=1 268,392 868 8,269

By a_chronic_kidney_disease

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_chronic_kidney_disease=0 5,415,678 0 0
2020-02-29 a_chronic_kidney_disease=1 211,842 0 0
2020-03-05 a_chronic_kidney_disease=0 5,414,480 41 1,440
2020-03-05 a_chronic_kidney_disease=1 211,651 9 216
2020-03-10 a_chronic_kidney_disease=0 5,412,861 104 3,220
2020-03-10 a_chronic_kidney_disease=1 211,438 29 411
2020-03-15 a_chronic_kidney_disease=0 5,407,958 399 7,968
2020-03-15 a_chronic_kidney_disease=1 211,032 81 779
2020-03-20 a_chronic_kidney_disease=0 5,391,344 1,521 27,177
2020-03-20 a_chronic_kidney_disease=1 210,224 344 1,549
2020-03-25 a_chronic_kidney_disease=0 5,372,036 3,058 45,147
2020-03-25 a_chronic_kidney_disease=1 209,132 606 2,448
2020-03-30 a_chronic_kidney_disease=0 5,357,039 4,383 59,340
2020-03-30 a_chronic_kidney_disease=1 207,928 865 3,398
2020-04-04 a_chronic_kidney_disease=0 5,339,574 5,203 72,130
2020-04-04 a_chronic_kidney_disease=1 206,402 1,059 4,586
2020-04-09 a_chronic_kidney_disease=0 5,329,185 5,895 83,170
2020-04-09 a_chronic_kidney_disease=1 205,298 1,229 5,676
2020-04-14 a_chronic_kidney_disease=0 5,322,027 6,303 90,202
2020-04-14 a_chronic_kidney_disease=1 204,389 1,344 6,366
2020-04-19 a_chronic_kidney_disease=0 5,311,447 6,580 98,240
2020-04-19 a_chronic_kidney_disease=1 202,979 1,416 7,556
2020-04-24 a_chronic_kidney_disease=0 5,303,686 6,794 106,629
2020-04-24 a_chronic_kidney_disease=1 202,025 1,482 8,519
2020-04-29 a_chronic_kidney_disease=0 5,298,621 6,913 111,782
2020-04-29 a_chronic_kidney_disease=1 201,439 1,513 9,168
2020-05-04 a_chronic_kidney_disease=0 5,294,888 7,011 115,012
2020-05-04 a_chronic_kidney_disease=1 200,927 1,541 9,481

By a_copd

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_copd=0 5,504,775 0 0
2020-02-29 a_copd=1 122,745 0 0
2020-03-05 a_copd=0 5,503,475 44 1,556
2020-03-05 a_copd=1 122,656 6 100
2020-03-10 a_copd=0 5,501,747 115 3,435
2020-03-10 a_copd=1 122,552 18 196
2020-03-15 a_copd=0 5,496,698 409 8,340
2020-03-15 a_copd=1 122,292 71 407
2020-03-20 a_copd=0 5,479,797 1,655 27,834
2020-03-20 a_copd=1 121,771 210 892
2020-03-25 a_copd=0 5,459,959 3,309 46,247
2020-03-25 a_copd=1 121,209 355 1,348
2020-03-30 a_copd=0 5,444,340 4,753 60,966
2020-03-30 a_copd=1 120,627 495 1,772
2020-04-04 a_copd=0 5,426,001 5,693 74,423
2020-04-04 a_copd=1 119,975 569 2,293
2020-04-09 a_copd=0 5,414,970 6,481 86,127
2020-04-09 a_copd=1 119,513 643 2,719
2020-04-14 a_copd=0 5,407,253 6,948 93,566
2020-04-14 a_copd=1 119,163 699 3,002
2020-04-19 a_copd=0 5,395,840 7,257 102,329
2020-04-19 a_copd=1 118,586 739 3,467
2020-04-24 a_copd=0 5,387,537 7,510 111,269
2020-04-24 a_copd=1 118,174 766 3,879
2020-04-29 a_copd=0 5,382,126 7,639 116,809
2020-04-29 a_copd=1 117,934 787 4,141
2020-05-04 a_copd=0 5,378,108 7,754 120,196
2020-05-04 a_copd=1 117,707 798 4,297

By a_dementia

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_dementia=0 5,570,210 0 0
2020-02-29 a_dementia=1 57,310 0 0
2020-03-05 a_dementia=0 5,568,939 48 1,526
2020-03-05 a_dementia=1 57,192 2 130
2020-03-10 a_dementia=0 5,567,221 127 3,387
2020-03-10 a_dementia=1 57,078 6 244
2020-03-15 a_dementia=0 5,562,101 461 8,333
2020-03-15 a_dementia=1 56,889 19 414
2020-03-20 a_dementia=0 5,544,920 1,777 28,043
2020-03-20 a_dementia=1 56,648 88 683
2020-03-25 a_dementia=0 5,524,879 3,513 46,554
2020-03-25 a_dementia=1 56,289 151 1,041
2020-03-30 a_dementia=0 5,509,252 5,025 61,173
2020-03-30 a_dementia=1 55,715 223 1,565
2020-04-04 a_dementia=0 5,491,324 5,973 74,214
2020-04-04 a_dementia=1 54,652 289 2,502
2020-04-09 a_dementia=0 5,480,691 6,730 85,413
2020-04-09 a_dementia=1 53,792 394 3,433
2020-04-14 a_dementia=0 5,473,567 7,181 92,309
2020-04-14 a_dementia=1 52,849 466 4,259
2020-04-19 a_dementia=0 5,463,121 7,488 100,188
2020-04-19 a_dementia=1 51,305 508 5,608
2020-04-24 a_dementia=0 5,455,345 7,727 108,614
2020-04-24 a_dementia=1 50,366 549 6,534
2020-04-29 a_dementia=0 5,450,193 7,852 113,840
2020-04-29 a_dementia=1 49,867 574 7,110
2020-05-04 a_dementia=0 5,446,370 7,957 117,128
2020-05-04 a_dementia=1 49,445 595 7,365

By a_heart_disease

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_heart_disease=0 5,079,725 0 0
2020-02-29 a_heart_disease=1 547,795 0 0
2020-03-05 a_heart_disease=0 5,078,692 31 1,259
2020-03-05 a_heart_disease=1 547,439 19 397
2020-03-10 a_heart_disease=0 5,077,250 82 2,849
2020-03-10 a_heart_disease=1 547,049 51 782
2020-03-15 a_heart_disease=0 5,072,893 309 7,103
2020-03-15 a_heart_disease=1 546,097 171 1,644
2020-03-20 a_heart_disease=0 5,057,540 1,235 24,985
2020-03-20 a_heart_disease=1 544,028 630 3,741
2020-03-25 a_heart_disease=0 5,039,789 2,477 41,649
2020-03-25 a_heart_disease=1 541,379 1,187 5,946
2020-03-30 a_heart_disease=0 5,026,102 3,574 54,776
2020-03-30 a_heart_disease=1 538,865 1,674 7,962
2020-04-04 a_heart_disease=0 5,010,132 4,269 66,406
2020-04-04 a_heart_disease=1 535,844 1,993 10,310
2020-04-09 a_heart_disease=0 5,000,662 4,831 76,571
2020-04-09 a_heart_disease=1 533,821 2,293 12,275
2020-04-14 a_heart_disease=0 4,994,212 5,139 82,999
2020-04-14 a_heart_disease=1 532,204 2,508 13,569
2020-04-19 a_heart_disease=0 4,984,742 5,359 90,149
2020-04-19 a_heart_disease=1 529,684 2,637 15,647
2020-04-24 a_heart_disease=0 4,977,727 5,549 97,740
2020-04-24 a_heart_disease=1 527,984 2,727 17,408
2020-04-29 a_heart_disease=0 4,973,141 5,639 102,363
2020-04-29 a_heart_disease=1 526,919 2,787 18,587
2020-05-04 a_heart_disease=0 4,969,819 5,715 105,330
2020-05-04 a_heart_disease=1 525,996 2,837 19,163

By a_hyperlipidemia

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_hyperlipidemia=0 5,106,683 0 0
2020-02-29 a_hyperlipidemia=1 520,837 0 0
2020-03-05 a_hyperlipidemia=0 5,105,416 42 1,516
2020-03-05 a_hyperlipidemia=1 520,715 8 140
2020-03-10 a_hyperlipidemia=0 5,103,770 113 3,297
2020-03-10 a_hyperlipidemia=1 520,529 20 334
2020-03-15 a_hyperlipidemia=0 5,098,984 395 7,903
2020-03-15 a_hyperlipidemia=1 520,006 85 844
2020-03-20 a_hyperlipidemia=0 5,083,527 1,494 25,772
2020-03-20 a_hyperlipidemia=1 518,041 371 2,954
2020-03-25 a_hyperlipidemia=0 5,065,469 2,932 42,609
2020-03-25 a_hyperlipidemia=1 515,699 732 4,986
2020-03-30 a_hyperlipidemia=0 5,051,255 4,205 56,028
2020-03-30 a_hyperlipidemia=1 513,712 1,043 6,710
2020-04-04 a_hyperlipidemia=0 5,034,476 5,053 68,419
2020-04-04 a_hyperlipidemia=1 511,500 1,209 8,297
2020-04-09 a_hyperlipidemia=0 5,024,309 5,764 79,210
2020-04-09 a_hyperlipidemia=1 510,174 1,360 9,636
2020-04-14 a_hyperlipidemia=0 5,017,120 6,205 86,125
2020-04-14 a_hyperlipidemia=1 509,296 1,442 10,443
2020-04-19 a_hyperlipidemia=0 5,006,528 6,503 94,262
2020-04-19 a_hyperlipidemia=1 507,898 1,493 11,534
2020-04-24 a_hyperlipidemia=0 4,998,802 6,724 102,604
2020-04-24 a_hyperlipidemia=1 506,909 1,552 12,544
2020-04-29 a_hyperlipidemia=0 4,993,771 6,857 107,738
2020-04-29 a_hyperlipidemia=1 506,289 1,569 13,212
2020-05-04 a_hyperlipidemia=0 4,989,987 6,965 110,927
2020-05-04 a_hyperlipidemia=1 505,828 1,587 13,566

By a_hypertension

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_hypertension=0 4,924,353 0 0
2020-02-29 a_hypertension=1 703,167 0 0
2020-03-05 a_hypertension=0 4,923,233 40 1,351
2020-03-05 a_hypertension=1 702,898 10 305
2020-03-10 a_hypertension=0 4,921,708 97 2,996
2020-03-10 a_hypertension=1 702,591 36 635
2020-03-15 a_hypertension=0 4,917,288 330 7,296
2020-03-15 a_hypertension=1 701,702 150 1,451
2020-03-20 a_hypertension=0 4,902,358 1,299 24,600
2020-03-20 a_hypertension=1 699,210 566 4,126
2020-03-25 a_hypertension=0 4,885,164 2,514 40,748
2020-03-25 a_hypertension=1 696,004 1,150 6,847
2020-03-30 a_hypertension=0 4,871,704 3,607 53,637
2020-03-30 a_hypertension=1 693,263 1,641 9,101
2020-04-04 a_hypertension=0 4,855,829 4,340 65,293
2020-04-04 a_hypertension=1 690,147 1,922 11,423
2020-04-09 a_hypertension=0 4,846,380 4,959 75,388
2020-04-09 a_hypertension=1 688,103 2,165 13,458
2020-04-14 a_hypertension=0 4,839,876 5,322 81,702
2020-04-14 a_hypertension=1 686,540 2,325 14,866
2020-04-19 a_hypertension=0 4,830,390 5,562 88,937
2020-04-19 a_hypertension=1 684,036 2,434 16,859
2020-04-24 a_hypertension=0 4,823,371 5,757 96,543
2020-04-24 a_hypertension=1 682,340 2,519 18,605
2020-04-29 a_hypertension=0 4,818,708 5,862 101,297
2020-04-29 a_hypertension=1 681,352 2,564 19,653
2020-05-04 a_hypertension=0 4,815,278 5,948 104,231
2020-05-04 a_hypertension=1 680,537 2,604 20,262

By a_malignant_neoplasm

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_malignant_neoplasm=0 5,330,111 0 0
2020-02-29 a_malignant_neoplasm=1 297,409 0 0
2020-03-05 a_malignant_neoplasm=0 5,328,988 41 1,362
2020-03-05 a_malignant_neoplasm=1 297,143 9 294
2020-03-10 a_malignant_neoplasm=0 5,327,436 103 3,065
2020-03-10 a_malignant_neoplasm=1 296,863 30 566
2020-03-15 a_malignant_neoplasm=0 5,322,764 387 7,608
2020-03-15 a_malignant_neoplasm=1 296,226 93 1,139
2020-03-20 a_malignant_neoplasm=0 5,306,486 1,527 26,374
2020-03-20 a_malignant_neoplasm=1 295,082 338 2,352
2020-03-25 a_malignant_neoplasm=0 5,287,532 3,022 44,039
2020-03-25 a_malignant_neoplasm=1 293,636 642 3,556
2020-03-30 a_malignant_neoplasm=0 5,272,714 4,363 58,120
2020-03-30 a_malignant_neoplasm=1 292,253 885 4,618
2020-04-04 a_malignant_neoplasm=0 5,255,239 5,236 70,861
2020-04-04 a_malignant_neoplasm=1 290,737 1,026 5,855
2020-04-09 a_malignant_neoplasm=0 5,244,805 5,971 81,986
2020-04-09 a_malignant_neoplasm=1 289,678 1,153 6,860
2020-04-14 a_malignant_neoplasm=0 5,237,487 6,397 89,105
2020-04-14 a_malignant_neoplasm=1 288,929 1,250 7,463
2020-04-19 a_malignant_neoplasm=0 5,226,756 6,677 97,302
2020-04-19 a_malignant_neoplasm=1 287,670 1,319 8,494
2020-04-24 a_malignant_neoplasm=0 5,218,863 6,924 105,757
2020-04-24 a_malignant_neoplasm=1 286,848 1,352 9,391
2020-04-29 a_malignant_neoplasm=0 5,213,776 7,044 110,921
2020-04-29 a_malignant_neoplasm=1 286,284 1,382 10,029
2020-05-04 a_malignant_neoplasm=0 5,210,049 7,153 114,150
2020-05-04 a_malignant_neoplasm=1 285,766 1,399 10,343

By a_obesity.5y

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_obesity.5y=0 4,689,927 0 0
2020-02-29 a_obesity.5y=1 937,593 0 0
2020-03-05 a_obesity.5y=0 4,688,791 26 1,377
2020-03-05 a_obesity.5y=1 937,340 24 279
2020-03-10 a_obesity.5y=0 4,687,286 80 3,008
2020-03-10 a_obesity.5y=1 937,013 53 623
2020-03-15 a_obesity.5y=0 4,683,041 286 7,130
2020-03-15 a_obesity.5y=1 935,949 194 1,617
2020-03-20 a_obesity.5y=0 4,669,182 1,103 23,239
2020-03-20 a_obesity.5y=1 932,386 762 5,487
2020-03-25 a_obesity.5y=0 4,653,385 2,179 38,157
2020-03-25 a_obesity.5y=1 927,783 1,485 9,438
2020-03-30 a_obesity.5y=0 4,641,111 3,078 50,003
2020-03-30 a_obesity.5y=1 923,856 2,170 12,735
2020-04-04 a_obesity.5y=0 4,626,405 3,702 60,888
2020-04-04 a_obesity.5y=1 919,571 2,560 15,828
2020-04-09 a_obesity.5y=0 4,617,461 4,268 70,476
2020-04-09 a_obesity.5y=1 917,022 2,856 18,370
2020-04-14 a_obesity.5y=0 4,611,157 4,591 76,585
2020-04-14 a_obesity.5y=1 915,259 3,056 19,983
2020-04-19 a_obesity.5y=0 4,601,856 4,809 83,815
2020-04-19 a_obesity.5y=1 912,570 3,187 21,981
2020-04-24 a_obesity.5y=0 4,594,963 4,998 91,261
2020-04-24 a_obesity.5y=1 910,748 3,278 23,887
2020-04-29 a_obesity.5y=0 4,590,483 5,091 95,831
2020-04-29 a_obesity.5y=1 909,577 3,335 25,119
2020-05-04 a_obesity.5y=0 4,587,126 5,177 98,698
2020-05-04 a_obesity.5y=1 908,689 3,375 25,795

By a_t2_diabetes

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_t2_diabetes=0 5,302,576 0 0
2020-02-29 a_t2_diabetes=1 324,944 0 0
2020-03-05 a_t2_diabetes=0 5,301,335 43 1,484
2020-03-05 a_t2_diabetes=1 324,796 7 172
2020-03-10 a_t2_diabetes=0 5,299,700 109 3,270
2020-03-10 a_t2_diabetes=1 324,599 24 361
2020-03-15 a_t2_diabetes=0 5,294,891 375 7,936
2020-03-15 a_t2_diabetes=1 324,099 105 811
2020-03-20 a_t2_diabetes=0 5,278,543 1,504 26,841
2020-03-20 a_t2_diabetes=1 323,025 361 1,885
2020-03-25 a_t2_diabetes=0 5,259,662 2,940 44,449
2020-03-25 a_t2_diabetes=1 321,506 724 3,146
2020-03-30 a_t2_diabetes=0 5,244,936 4,175 58,459
2020-03-30 a_t2_diabetes=1 320,031 1,073 4,279
2020-04-04 a_t2_diabetes=0 5,227,607 4,988 71,229
2020-04-04 a_t2_diabetes=1 318,369 1,274 5,487
2020-04-09 a_t2_diabetes=0 5,217,221 5,669 82,316
2020-04-09 a_t2_diabetes=1 317,262 1,455 6,530
2020-04-14 a_t2_diabetes=0 5,209,983 6,070 89,351
2020-04-14 a_t2_diabetes=1 316,433 1,577 7,217
2020-04-19 a_t2_diabetes=0 5,199,365 6,334 97,502
2020-04-19 a_t2_diabetes=1 315,061 1,662 8,294
2020-04-24 a_t2_diabetes=0 5,191,510 6,561 105,957
2020-04-24 a_t2_diabetes=1 314,201 1,715 9,191
2020-04-29 a_t2_diabetes=0 5,186,384 6,673 111,220
2020-04-29 a_t2_diabetes=1 313,676 1,753 9,730
2020-05-04 a_t2_diabetes=0 5,182,569 6,783 114,463
2020-05-04 a_t2_diabetes=1 313,246 1,769 10,030

Transition 3: From general population to death

overall

Date Number at risk Cumulative events Cumulative censored
2020-02-29 5,627,520 0 0
2020-03-05 5,626,131 600 1,106
2020-03-10 5,624,299 1,069 2,695
2020-03-15 5,618,990 1,881 7,346
2020-03-20 5,601,568 2,600 27,991
2020-03-25 5,581,168 3,182 48,077
2020-03-30 5,564,967 3,731 64,255
2020-04-04 5,545,976 4,993 77,985
2020-04-09 5,534,483 5,930 90,040
2020-04-14 5,526,416 6,287 97,928
2020-04-19 5,514,426 7,752 106,040
2020-04-24 5,505,711 9,065 114,359
2020-04-29 5,500,060 10,186 119,190
2020-05-04 5,495,815 10,738 122,307

By gender

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 gender=Male 2,768,246 0 0
2020-02-29 gender=Female 2,859,274 0 0
2020-03-05 gender=Male 2,767,555 311 547
2020-03-05 gender=Female 2,858,576 289 559
2020-03-10 gender=Male 2,766,634 558 1,311
2020-03-10 gender=Female 2,857,665 511 1,384
2020-03-15 gender=Male 2,764,197 980 3,388
2020-03-15 gender=Female 2,854,793 901 3,958
2020-03-20 gender=Male 2,756,157 1,345 12,836
2020-03-20 gender=Female 2,845,411 1,255 15,155
2020-03-25 gender=Male 2,746,832 1,658 22,017
2020-03-25 gender=Female 2,834,336 1,524 26,060
2020-03-30 gender=Male 2,739,460 1,934 29,204
2020-03-30 gender=Female 2,825,507 1,797 35,051
2020-04-04 gender=Male 2,731,513 2,571 34,754
2020-04-04 gender=Female 2,814,463 2,422 43,231
2020-04-09 gender=Male 2,726,839 3,056 39,556
2020-04-09 gender=Female 2,807,644 2,874 50,484
2020-04-14 gender=Male 2,723,689 3,198 42,624
2020-04-14 gender=Female 2,802,727 3,089 55,304
2020-04-19 gender=Male 2,718,947 3,880 45,675
2020-04-19 gender=Female 2,795,479 3,872 60,365
2020-04-24 gender=Male 2,715,537 4,513 48,901
2020-04-24 gender=Female 2,790,174 4,552 65,458
2020-04-29 gender=Male 2,713,248 5,052 50,790
2020-04-29 gender=Female 2,786,812 5,134 68,400
2020-05-04 gender=Male 2,711,351 5,339 52,176
2020-05-04 gender=Female 2,784,464 5,399 70,131

By age

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 age_gr=Under 18 967,227 0 0
2020-02-29 age_gr=18 to 39 1,438,732 0 0
2020-02-29 age_gr=40 to 59 1,788,832 0 0
2020-02-29 age_gr=60 to 69 617,929 0 0
2020-02-29 age_gr=70 to 79 474,369 0 0
2020-02-29 age_gr=80 or older 340,431 0 0
2020-03-05 age_gr=Under 18 967,148 0 99
2020-03-05 age_gr=18 to 39 1,438,396 5 442
2020-03-05 age_gr=40 to 59 1,788,516 40 344
2020-03-05 age_gr=60 to 69 617,797 73 97
2020-03-05 age_gr=70 to 79 474,235 102 56
2020-03-05 age_gr=80 or older 340,039 380 68
2020-03-10 age_gr=Under 18 967,057 0 183
2020-03-10 age_gr=18 to 39 1,437,927 7 952
2020-03-10 age_gr=40 to 59 1,788,016 76 948
2020-03-10 age_gr=60 to 69 617,593 126 272
2020-03-10 age_gr=70 to 79 474,071 174 168
2020-03-10 age_gr=80 or older 339,635 686 172
2020-03-15 age_gr=Under 18 966,925 2 312
2020-03-15 age_gr=18 to 39 1,436,495 15 2,385
2020-03-15 age_gr=40 to 59 1,786,021 141 2,954
2020-03-15 age_gr=60 to 69 617,051 206 757
2020-03-15 age_gr=70 to 79 473,621 328 516
2020-03-15 age_gr=80 or older 338,877 1,189 422
2020-03-20 age_gr=Under 18 966,550 2 807
2020-03-20 age_gr=18 to 39 1,431,489 24 8,449
2020-03-20 age_gr=40 to 59 1,778,104 190 12,436
2020-03-20 age_gr=60 to 69 615,231 274 3,000
2020-03-20 age_gr=70 to 79 472,402 437 1,939
2020-03-20 age_gr=80 or older 337,792 1,673 1,360
2020-03-25 age_gr=Under 18 966,050 4 1,321
2020-03-25 age_gr=18 to 39 1,426,062 31 13,885
2020-03-25 age_gr=40 to 59 1,769,519 226 20,977
2020-03-25 age_gr=60 to 69 612,789 333 5,382
2020-03-25 age_gr=70 to 79 470,557 533 3,749
2020-03-25 age_gr=80 or older 336,191 2,055 2,763
2020-03-30 age_gr=Under 18 965,564 4 1,805
2020-03-30 age_gr=18 to 39 1,422,131 36 17,985
2020-03-30 age_gr=40 to 59 1,763,306 257 27,616
2020-03-30 age_gr=60 to 69 610,854 386 7,278
2020-03-30 age_gr=70 to 79 468,883 627 5,225
2020-03-30 age_gr=80 or older 334,229 2,421 4,346
2020-04-04 age_gr=Under 18 964,820 5 2,420
2020-04-04 age_gr=18 to 39 1,417,358 45 21,612
2020-04-04 age_gr=40 to 59 1,756,123 328 32,776
2020-04-04 age_gr=60 to 69 608,859 498 8,718
2020-04-04 age_gr=70 to 79 467,358 847 6,344
2020-04-04 age_gr=80 or older 331,458 3,270 6,115
2020-04-09 age_gr=Under 18 964,348 5 3,027
2020-04-09 age_gr=18 to 39 1,414,606 50 24,705
2020-04-09 age_gr=40 to 59 1,752,126 375 37,200
2020-04-09 age_gr=60 to 69 607,762 583 9,860
2020-04-09 age_gr=70 to 79 466,393 1,023 7,211
2020-04-09 age_gr=80 or older 329,248 3,894 8,037
2020-04-14 age_gr=Under 18 963,998 5 3,382
2020-04-14 age_gr=18 to 39 1,412,804 50 26,650
2020-04-14 age_gr=40 to 59 1,749,659 398 39,888
2020-04-14 age_gr=60 to 69 607,070 608 10,523
2020-04-14 age_gr=70 to 79 465,724 1,089 7,784
2020-04-14 age_gr=80 or older 327,161 4,137 9,701
2020-04-19 age_gr=Under 18 963,456 6 3,791
2020-04-19 age_gr=18 to 39 1,410,279 55 28,526
2020-04-19 age_gr=40 to 59 1,746,184 460 42,349
2020-04-19 age_gr=60 to 69 606,044 711 11,225
2020-04-19 age_gr=70 to 79 464,575 1,353 8,506
2020-04-19 age_gr=80 or older 323,888 5,167 11,643
2020-04-24 age_gr=Under 18 963,003 7 4,298
2020-04-24 age_gr=18 to 39 1,408,187 63 30,854
2020-04-24 age_gr=40 to 59 1,743,638 515 45,160
2020-04-24 age_gr=60 to 69 605,380 824 11,894
2020-04-24 age_gr=70 to 79 463,869 1,557 9,092
2020-04-24 age_gr=80 or older 321,634 6,099 13,061
2020-04-29 age_gr=Under 18 962,652 7 4,665
2020-04-29 age_gr=18 to 39 1,406,826 74 32,184
2020-04-29 age_gr=40 to 59 1,741,926 582 46,814
2020-04-29 age_gr=60 to 69 604,888 927 12,288
2020-04-29 age_gr=70 to 79 463,397 1,762 9,401
2020-04-29 age_gr=80 or older 320,371 6,834 13,838
2020-05-04 age_gr=Under 18 962,391 9 4,930
2020-05-04 age_gr=18 to 39 1,405,894 75 33,108
2020-05-04 age_gr=40 to 59 1,740,729 613 47,939
2020-05-04 age_gr=60 to 69 604,521 975 12,554
2020-05-04 age_gr=70 to 79 463,006 1,865 9,591
2020-05-04 age_gr=80 or older 319,274 7,201 14,185

By charlson

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 charlson=0 4,572,265 0 0
2020-02-29 charlson=1 410,497 0 0
2020-02-29 charlson=2 363,057 0 0
2020-02-29 charlson=3+ 281,701 0 0
2020-03-05 charlson=0 4,571,509 69 897
2020-03-05 charlson=1 410,364 82 80
2020-03-05 charlson=2 362,897 116 73
2020-03-05 charlson=3+ 281,361 333 56
2020-03-10 charlson=0 4,570,392 135 2,126
2020-03-10 charlson=1 410,196 134 219
2020-03-10 charlson=2 362,693 217 185
2020-03-10 charlson=3+ 281,018 583 165
2020-03-15 charlson=0 4,566,799 225 5,732
2020-03-15 charlson=1 409,735 225 618
2020-03-15 charlson=2 362,184 380 549
2020-03-15 charlson=3+ 280,272 1,051 447
2020-03-20 charlson=0 4,553,390 322 22,005
2020-03-20 charlson=1 408,137 313 2,457
2020-03-20 charlson=2 360,932 522 1,974
2020-03-20 charlson=3+ 279,109 1,443 1,555
2020-03-25 charlson=0 4,538,019 399 37,341
2020-03-25 charlson=1 406,294 403 4,285
2020-03-25 charlson=2 359,276 639 3,549
2020-03-25 charlson=3+ 277,579 1,741 2,902
2020-03-30 charlson=0 4,526,633 460 49,211
2020-03-30 charlson=1 404,704 472 5,809
2020-03-30 charlson=2 357,781 760 4,933
2020-03-30 charlson=3+ 275,849 2,039 4,302
2020-04-04 charlson=0 4,513,349 655 59,061
2020-04-04 charlson=1 402,809 659 7,184
2020-04-04 charlson=2 356,155 1,010 6,062
2020-04-04 charlson=3+ 273,663 2,669 5,678
2020-04-09 charlson=0 4,505,766 805 67,487
2020-04-09 charlson=1 401,582 794 8,451
2020-04-09 charlson=2 355,075 1,213 7,097
2020-04-09 charlson=3+ 272,060 3,118 7,005
2020-04-14 charlson=0 4,500,816 845 72,706
2020-04-14 charlson=1 400,626 856 9,364
2020-04-14 charlson=2 354,220 1,290 7,817
2020-04-14 charlson=3+ 270,754 3,296 8,041
2020-04-19 charlson=0 4,493,753 1,064 77,815
2020-04-19 charlson=1 399,158 1,073 10,354
2020-04-19 charlson=2 352,951 1,573 8,623
2020-04-19 charlson=3+ 268,564 4,042 9,248
2020-04-24 charlson=0 4,488,361 1,243 83,654
2020-04-24 charlson=1 398,135 1,288 11,248
2020-04-24 charlson=2 352,068 1,860 9,300
2020-04-24 charlson=3+ 267,147 4,674 10,157
2020-04-29 charlson=0 4,484,759 1,422 87,088
2020-04-29 charlson=1 397,529 1,467 11,748
2020-04-29 charlson=2 351,520 2,089 9,695
2020-04-29 charlson=3+ 266,252 5,208 10,659
2020-05-04 charlson=0 4,482,261 1,504 89,428
2020-05-04 charlson=1 397,044 1,535 12,051
2020-05-04 charlson=2 351,026 2,224 9,921
2020-05-04 charlson=3+ 265,484 5,475 10,907

By a_autoimmune_condition

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_autoimmune_condition=0 5,350,070 0 0
2020-02-29 a_autoimmune_condition=1 277,450 0 0
2020-03-05 a_autoimmune_condition=0 5,348,777 527 1,059
2020-03-05 a_autoimmune_condition=1 277,354 73 47
2020-03-10 a_autoimmune_condition=0 5,347,098 931 2,552
2020-03-10 a_autoimmune_condition=1 277,201 138 143
2020-03-15 a_autoimmune_condition=0 5,342,174 1,639 6,903
2020-03-15 a_autoimmune_condition=1 276,816 242 443
2020-03-20 a_autoimmune_condition=0 5,325,892 2,280 26,239
2020-03-20 a_autoimmune_condition=1 275,676 320 1,752
2020-03-25 a_autoimmune_condition=0 5,306,825 2,808 45,039
2020-03-25 a_autoimmune_condition=1 274,343 374 3,038
2020-03-30 a_autoimmune_condition=0 5,291,709 3,276 60,169
2020-03-30 a_autoimmune_condition=1 273,258 455 4,086
2020-04-04 a_autoimmune_condition=0 5,274,026 4,402 72,981
2020-04-04 a_autoimmune_condition=1 271,950 591 5,004
2020-04-09 a_autoimmune_condition=0 5,263,311 5,228 84,235
2020-04-09 a_autoimmune_condition=1 271,172 702 5,805
2020-04-14 a_autoimmune_condition=0 5,255,822 5,544 91,589
2020-04-14 a_autoimmune_condition=1 270,594 743 6,339
2020-04-19 a_autoimmune_condition=0 5,244,775 6,851 99,090
2020-04-19 a_autoimmune_condition=1 269,651 901 6,950
2020-04-24 a_autoimmune_condition=0 5,236,645 8,029 106,911
2020-04-24 a_autoimmune_condition=1 269,066 1,036 7,448
2020-04-29 a_autoimmune_condition=0 5,231,350 9,031 111,462
2020-04-29 a_autoimmune_condition=1 268,710 1,155 7,728
2020-05-04 a_autoimmune_condition=0 5,227,423 9,513 114,395
2020-05-04 a_autoimmune_condition=1 268,392 1,225 7,912

By a_chronic_kidney_disease

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_chronic_kidney_disease=0 5,415,678 0 0
2020-02-29 a_chronic_kidney_disease=1 211,842 0 0
2020-03-05 a_chronic_kidney_disease=0 5,414,480 408 1,073
2020-03-05 a_chronic_kidney_disease=1 211,651 192 33
2020-03-10 a_chronic_kidney_disease=0 5,412,861 725 2,599
2020-03-10 a_chronic_kidney_disease=1 211,438 344 96
2020-03-15 a_chronic_kidney_disease=0 5,407,958 1,289 7,078
2020-03-15 a_chronic_kidney_disease=1 211,032 592 268
2020-03-20 a_chronic_kidney_disease=0 5,391,344 1,749 26,949
2020-03-20 a_chronic_kidney_disease=1 210,224 851 1,042
2020-03-25 a_chronic_kidney_disease=0 5,372,036 2,132 46,073
2020-03-25 a_chronic_kidney_disease=1 209,132 1,050 2,004
2020-03-30 a_chronic_kidney_disease=0 5,357,039 2,470 61,253
2020-03-30 a_chronic_kidney_disease=1 207,928 1,261 3,002
2020-04-04 a_chronic_kidney_disease=0 5,339,574 3,309 74,024
2020-04-04 a_chronic_kidney_disease=1 206,402 1,684 3,961
2020-04-09 a_chronic_kidney_disease=0 5,329,185 3,924 85,141
2020-04-09 a_chronic_kidney_disease=1 205,298 2,006 4,899
2020-04-14 a_chronic_kidney_disease=0 5,322,027 4,157 92,348
2020-04-14 a_chronic_kidney_disease=1 204,389 2,130 5,580
2020-04-19 a_chronic_kidney_disease=0 5,311,447 5,139 99,681
2020-04-19 a_chronic_kidney_disease=1 202,979 2,613 6,359
2020-04-24 a_chronic_kidney_disease=0 5,303,686 6,028 107,395
2020-04-24 a_chronic_kidney_disease=1 202,025 3,037 6,964
2020-04-29 a_chronic_kidney_disease=0 5,298,621 6,806 111,889
2020-04-29 a_chronic_kidney_disease=1 201,439 3,380 7,301
2020-05-04 a_chronic_kidney_disease=0 5,294,888 7,173 114,850
2020-05-04 a_chronic_kidney_disease=1 200,927 3,565 7,457

By a_copd

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_copd=0 5,504,775 0 0
2020-02-29 a_copd=1 122,745 0 0
2020-03-05 a_copd=0 5,503,475 518 1,082
2020-03-05 a_copd=1 122,656 82 24
2020-03-10 a_copd=0 5,501,747 926 2,624
2020-03-10 a_copd=1 122,552 143 71
2020-03-15 a_copd=0 5,496,698 1,607 7,142
2020-03-15 a_copd=1 122,292 274 204
2020-03-20 a_copd=0 5,479,797 2,218 27,271
2020-03-20 a_copd=1 121,771 382 720
2020-03-25 a_copd=0 5,459,959 2,722 46,834
2020-03-25 a_copd=1 121,209 460 1,243
2020-03-30 a_copd=0 5,444,340 3,200 62,519
2020-03-30 a_copd=1 120,627 531 1,736
2020-04-04 a_copd=0 5,426,001 4,294 75,822
2020-04-04 a_copd=1 119,975 699 2,163
2020-04-09 a_copd=0 5,414,970 5,111 87,497
2020-04-09 a_copd=1 119,513 819 2,543
2020-04-14 a_copd=0 5,407,253 5,430 95,084
2020-04-14 a_copd=1 119,163 857 2,844
2020-04-19 a_copd=0 5,395,840 6,725 102,861
2020-04-19 a_copd=1 118,586 1,027 3,179
2020-04-24 a_copd=0 5,387,537 7,888 110,891
2020-04-24 a_copd=1 118,174 1,177 3,468
2020-04-29 a_copd=0 5,382,126 8,878 115,570
2020-04-29 a_copd=1 117,934 1,308 3,620
2020-05-04 a_copd=0 5,378,108 9,347 118,603
2020-05-04 a_copd=1 117,707 1,391 3,704

By a_dementia

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_dementia=0 5,570,210 0 0
2020-02-29 a_dementia=1 57,310 0 0
2020-03-05 a_dementia=0 5,568,939 486 1,088
2020-03-05 a_dementia=1 57,192 114 18
2020-03-10 a_dementia=0 5,567,221 859 2,655
2020-03-10 a_dementia=1 57,078 210 40
2020-03-15 a_dementia=0 5,562,101 1,531 7,263
2020-03-15 a_dementia=1 56,889 350 83
2020-03-20 a_dementia=0 5,544,920 2,117 27,703
2020-03-20 a_dementia=1 56,648 483 288
2020-03-25 a_dementia=0 5,524,879 2,602 47,465
2020-03-25 a_dementia=1 56,289 580 612
2020-03-30 a_dementia=0 5,509,252 3,054 63,144
2020-03-30 a_dementia=1 55,715 677 1,111
2020-04-04 a_dementia=0 5,491,324 4,026 76,161
2020-04-04 a_dementia=1 54,652 967 1,824
2020-04-09 a_dementia=0 5,480,691 4,735 87,408
2020-04-09 a_dementia=1 53,792 1,195 2,632
2020-04-14 a_dementia=0 5,473,567 4,973 94,517
2020-04-14 a_dementia=1 52,849 1,314 3,411
2020-04-19 a_dementia=0 5,463,121 5,997 101,679
2020-04-19 a_dementia=1 51,305 1,755 4,361
2020-04-24 a_dementia=0 5,455,345 6,919 109,422
2020-04-24 a_dementia=1 50,366 2,146 4,937
2020-04-29 a_dementia=0 5,450,193 7,758 113,934
2020-04-29 a_dementia=1 49,867 2,428 5,256
2020-05-04 a_dementia=0 5,446,370 8,175 116,910
2020-05-04 a_dementia=1 49,445 2,563 5,397

By a_heart_disease

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_heart_disease=0 5,079,725 0 0
2020-02-29 a_heart_disease=1 547,795 0 0
2020-03-05 a_heart_disease=0 5,078,692 281 1,009
2020-03-05 a_heart_disease=1 547,439 319 97
2020-03-10 a_heart_disease=0 5,077,250 510 2,421
2020-03-10 a_heart_disease=1 547,049 559 274
2020-03-15 a_heart_disease=0 5,072,893 872 6,540
2020-03-15 a_heart_disease=1 546,097 1,009 806
2020-03-20 a_heart_disease=0 5,057,540 1,222 24,998
2020-03-20 a_heart_disease=1 544,028 1,378 2,993
2020-03-25 a_heart_disease=0 5,039,789 1,490 42,636
2020-03-25 a_heart_disease=1 541,379 1,692 5,441
2020-03-30 a_heart_disease=0 5,026,102 1,746 56,604
2020-03-30 a_heart_disease=1 538,865 1,985 7,651
2020-04-04 a_heart_disease=0 5,010,132 2,368 68,307
2020-04-04 a_heart_disease=1 535,844 2,625 9,678
2020-04-09 a_heart_disease=0 5,000,662 2,847 78,555
2020-04-09 a_heart_disease=1 533,821 3,083 11,485
2020-04-14 a_heart_disease=0 4,994,212 3,035 85,103
2020-04-14 a_heart_disease=1 532,204 3,252 12,825
2020-04-19 a_heart_disease=0 4,984,742 3,775 91,733
2020-04-19 a_heart_disease=1 529,684 3,977 14,307
2020-04-24 a_heart_disease=0 4,977,727 4,462 98,827
2020-04-24 a_heart_disease=1 527,984 4,603 15,532
2020-04-29 a_heart_disease=0 4,973,141 5,053 102,949
2020-04-29 a_heart_disease=1 526,919 5,133 16,241
2020-05-04 a_heart_disease=0 4,969,819 5,335 105,710
2020-05-04 a_heart_disease=1 525,996 5,403 16,597

By a_hyperlipidemia

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_hyperlipidemia=0 5,106,683 0 0
2020-02-29 a_hyperlipidemia=1 520,837 0 0
2020-03-05 a_hyperlipidemia=0 5,105,416 529 1,029
2020-03-05 a_hyperlipidemia=1 520,715 71 77
2020-03-10 a_hyperlipidemia=0 5,103,770 931 2,479
2020-03-10 a_hyperlipidemia=1 520,529 138 216
2020-03-15 a_hyperlipidemia=0 5,098,984 1,637 6,661
2020-03-15 a_hyperlipidemia=1 520,006 244 685
2020-03-20 a_hyperlipidemia=0 5,083,527 2,259 25,007
2020-03-20 a_hyperlipidemia=1 518,041 341 2,984
2020-03-25 a_hyperlipidemia=0 5,065,469 2,749 42,792
2020-03-25 a_hyperlipidemia=1 515,699 433 5,285
2020-03-30 a_hyperlipidemia=0 5,051,255 3,216 57,017
2020-03-30 a_hyperlipidemia=1 513,712 515 7,238
2020-04-04 a_hyperlipidemia=0 5,034,476 4,315 69,157
2020-04-04 a_hyperlipidemia=1 511,500 678 8,828
2020-04-09 a_hyperlipidemia=0 5,024,309 5,121 79,853
2020-04-09 a_hyperlipidemia=1 510,174 809 10,187
2020-04-14 a_hyperlipidemia=0 5,017,120 5,431 86,899
2020-04-14 a_hyperlipidemia=1 509,296 856 11,029
2020-04-19 a_hyperlipidemia=0 5,006,528 6,686 94,079
2020-04-19 a_hyperlipidemia=1 507,898 1,066 11,961
2020-04-24 a_hyperlipidemia=0 4,998,802 7,834 101,494
2020-04-24 a_hyperlipidemia=1 506,909 1,231 12,865
2020-04-29 a_hyperlipidemia=0 4,993,771 8,781 105,814
2020-04-29 a_hyperlipidemia=1 506,289 1,405 13,376
2020-05-04 a_hyperlipidemia=0 4,989,987 9,266 108,626
2020-05-04 a_hyperlipidemia=1 505,828 1,472 13,681

By a_hypertension

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_hypertension=0 4,924,353 0 0
2020-02-29 a_hypertension=1 703,167 0 0
2020-03-05 a_hypertension=0 4,923,233 387 1,004
2020-03-05 a_hypertension=1 702,898 213 102
2020-03-10 a_hypertension=0 4,921,708 708 2,385
2020-03-10 a_hypertension=1 702,591 361 310
2020-03-15 a_hypertension=0 4,917,288 1,257 6,369
2020-03-15 a_hypertension=1 701,702 624 977
2020-03-20 a_hypertension=0 4,902,358 1,732 24,167
2020-03-20 a_hypertension=1 699,210 868 3,824
2020-03-25 a_hypertension=0 4,885,164 2,128 41,134
2020-03-25 a_hypertension=1 696,004 1,054 6,943
2020-03-30 a_hypertension=0 4,871,704 2,480 54,764
2020-03-30 a_hypertension=1 693,263 1,251 9,491
2020-04-04 a_hypertension=0 4,855,829 3,296 66,337
2020-04-04 a_hypertension=1 690,147 1,697 11,648
2020-04-09 a_hypertension=0 4,846,380 3,919 76,428
2020-04-09 a_hypertension=1 688,103 2,011 13,612
2020-04-14 a_hypertension=0 4,839,876 4,165 82,859
2020-04-14 a_hypertension=1 686,540 2,122 15,069
2020-04-19 a_hypertension=0 4,830,390 5,169 89,330
2020-04-19 a_hypertension=1 684,036 2,583 16,710
2020-04-24 a_hypertension=0 4,823,371 6,021 96,279
2020-04-24 a_hypertension=1 682,340 3,044 18,080
2020-04-29 a_hypertension=0 4,818,708 6,780 100,379
2020-04-29 a_hypertension=1 681,352 3,406 18,811
2020-05-04 a_hypertension=0 4,815,278 7,140 103,039
2020-05-04 a_hypertension=1 680,537 3,598 19,268

By a_malignant_neoplasm

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_malignant_neoplasm=0 5,330,111 0 0
2020-02-29 a_malignant_neoplasm=1 297,409 0 0
2020-03-05 a_malignant_neoplasm=0 5,328,988 357 1,046
2020-03-05 a_malignant_neoplasm=1 297,143 243 60
2020-03-10 a_malignant_neoplasm=0 5,327,436 629 2,539
2020-03-10 a_malignant_neoplasm=1 296,863 440 156
2020-03-15 a_malignant_neoplasm=0 5,322,764 1,111 6,884
2020-03-15 a_malignant_neoplasm=1 296,226 770 462
2020-03-20 a_malignant_neoplasm=0 5,306,486 1,561 26,340
2020-03-20 a_malignant_neoplasm=1 295,082 1,039 1,651
2020-03-25 a_malignant_neoplasm=0 5,287,532 1,937 45,124
2020-03-25 a_malignant_neoplasm=1 293,636 1,245 2,953
2020-03-30 a_malignant_neoplasm=0 5,272,714 2,292 60,191
2020-03-30 a_malignant_neoplasm=1 292,253 1,439 4,064
2020-04-04 a_malignant_neoplasm=0 5,255,239 3,155 72,942
2020-04-04 a_malignant_neoplasm=1 290,737 1,838 5,043
2020-04-09 a_malignant_neoplasm=0 5,244,805 3,822 84,135
2020-04-09 a_malignant_neoplasm=1 289,678 2,108 5,905
2020-04-14 a_malignant_neoplasm=0 5,237,487 4,070 91,432
2020-04-14 a_malignant_neoplasm=1 288,929 2,217 6,496
2020-04-19 a_malignant_neoplasm=0 5,226,756 5,111 98,868
2020-04-19 a_malignant_neoplasm=1 287,670 2,641 7,172
2020-04-24 a_malignant_neoplasm=0 5,218,863 6,050 106,631
2020-04-24 a_malignant_neoplasm=1 286,848 3,015 7,728
2020-04-29 a_malignant_neoplasm=0 5,213,776 6,814 111,151
2020-04-29 a_malignant_neoplasm=1 286,284 3,372 8,039
2020-05-04 a_malignant_neoplasm=0 5,210,049 7,192 114,111
2020-05-04 a_malignant_neoplasm=1 285,766 3,546 8,196

By a_obesity.5y

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_obesity.5y=0 4,689,927 0 0
2020-02-29 a_obesity.5y=1 937,593 0 0
2020-03-05 a_obesity.5y=0 4,688,791 440 963
2020-03-05 a_obesity.5y=1 937,340 160 143
2020-03-10 a_obesity.5y=0 4,687,286 785 2,303
2020-03-10 a_obesity.5y=1 937,013 284 392
2020-03-15 a_obesity.5y=0 4,683,041 1,355 6,061
2020-03-15 a_obesity.5y=1 935,949 526 1,285
2020-03-20 a_obesity.5y=0 4,669,182 1,863 22,479
2020-03-20 a_obesity.5y=1 932,386 737 5,512
2020-03-25 a_obesity.5y=0 4,653,385 2,255 38,081
2020-03-25 a_obesity.5y=1 927,783 927 9,996
2020-03-30 a_obesity.5y=0 4,641,111 2,629 50,452
2020-03-30 a_obesity.5y=1 923,856 1,102 13,803
2020-04-04 a_obesity.5y=0 4,626,405 3,510 61,080
2020-04-04 a_obesity.5y=1 919,571 1,483 16,905
2020-04-09 a_obesity.5y=0 4,617,461 4,195 70,549
2020-04-09 a_obesity.5y=1 917,022 1,735 19,491
2020-04-14 a_obesity.5y=0 4,611,157 4,463 76,713
2020-04-14 a_obesity.5y=1 915,259 1,824 21,215
2020-04-19 a_obesity.5y=0 4,601,856 5,530 83,094
2020-04-19 a_obesity.5y=1 912,570 2,222 22,946
2020-04-24 a_obesity.5y=0 4,594,963 6,500 89,759
2020-04-24 a_obesity.5y=1 910,748 2,565 24,600
2020-04-29 a_obesity.5y=0 4,590,483 7,308 93,614
2020-04-29 a_obesity.5y=1 909,577 2,878 25,576
2020-05-04 a_obesity.5y=0 4,587,126 7,722 96,153
2020-05-04 a_obesity.5y=1 908,689 3,016 26,154

By a_t2_diabetes

Date Strata Number at risk Cumulative events Cumulative censored
2020-02-29 a_t2_diabetes=0 5,302,576 0 0
2020-02-29 a_t2_diabetes=1 324,944 0 0
2020-03-05 a_t2_diabetes=0 5,301,335 468 1,059
2020-03-05 a_t2_diabetes=1 324,796 132 47
2020-03-10 a_t2_diabetes=0 5,299,700 832 2,547
2020-03-10 a_t2_diabetes=1 324,599 237 148
2020-03-15 a_t2_diabetes=0 5,294,891 1,437 6,874
2020-03-15 a_t2_diabetes=1 324,099 444 472
2020-03-20 a_t2_diabetes=0 5,278,543 1,980 26,365
2020-03-20 a_t2_diabetes=1 323,025 620 1,626
2020-03-25 a_t2_diabetes=0 5,259,662 2,428 44,961
2020-03-25 a_t2_diabetes=1 321,506 754 3,116
2020-03-30 a_t2_diabetes=0 5,244,936 2,832 59,802
2020-03-30 a_t2_diabetes=1 320,031 899 4,453
2020-04-04 a_t2_diabetes=0 5,227,607 3,799 72,418
2020-04-04 a_t2_diabetes=1 318,369 1,194 5,567
2020-04-09 a_t2_diabetes=0 5,217,221 4,507 83,478
2020-04-09 a_t2_diabetes=1 317,262 1,423 6,562
2020-04-14 a_t2_diabetes=0 5,209,983 4,779 90,642
2020-04-14 a_t2_diabetes=1 316,433 1,508 7,286
2020-04-19 a_t2_diabetes=0 5,199,365 5,902 97,934
2020-04-19 a_t2_diabetes=1 315,061 1,850 8,106
2020-04-24 a_t2_diabetes=0 5,191,510 6,935 105,583
2020-04-24 a_t2_diabetes=1 314,201 2,130 8,776
2020-04-29 a_t2_diabetes=0 5,186,384 7,838 110,055
2020-04-29 a_t2_diabetes=1 313,676 2,348 9,135
2020-05-04 a_t2_diabetes=0 5,182,569 8,268 112,978
2020-05-04 a_t2_diabetes=1 313,246 2,470 9,329

Transition 4: From diagnosed with COVID-19 to hospitalised with COVID-19

By gender

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 gender=Male 44,565 0 0
0 gender=Female 63,993 0 0
5 gender=Male 41,060 2,888 1,257
5 gender=Female 60,633 2,333 1,722
10 gender=Male 36,965 4,593 3,311
10 gender=Female 55,664 3,806 4,901
15 gender=Male 34,632 4,924 5,609
15 gender=Female 52,149 4,144 8,682
20 gender=Male 31,995 5,019 8,264
20 gender=Female 47,493 4,284 13,416
25 gender=Male 28,740 5,049 11,065
25 gender=Female 42,176 4,331 18,008
30 gender=Male 25,654 5,068 15,009
30 gender=Female 37,016 4,344 24,484
35 gender=Male 22,249 5,077 18,286
35 gender=Female 31,315 4,352 29,971
40 gender=Male 17,693 5,078 23,134
40 gender=Female 24,345 4,356 37,107
45 gender=Male 9,102 5,079 30,680
45 gender=Female 12,598 4,357 47,477
50 gender=Male 4,068 5,080 36,603
50 gender=Female 5,752 4,357 55,418
55 gender=Male 594 5,080 39,107
55 gender=Female 995 4,357 58,989
60 gender=Male 65 5,080 39,427
60 gender=Female 154 4,357 59,493
65 gender=Male 7 5,080 39,484
65 gender=Female 36 4,357 59,630

By age

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 age_gr=18 to 39 30,379 0 0
0 age_gr=40 to 59 44,510 0 0
0 age_gr=60 to 69 10,593 0 0
0 age_gr=70 to 79 7,104 0 0
0 age_gr=80 or older 11,501 0 0
5 age_gr=18 to 39 29,363 436 740
5 age_gr=40 to 59 42,300 1,736 960
5 age_gr=60 to 69 9,558 999 237
5 age_gr=70 to 79 5,999 1,088 224
5 age_gr=80 or older 10,237 941 589
10 age_gr=18 to 39 27,661 771 2,036
10 age_gr=40 to 59 39,005 3,045 2,660
10 age_gr=60 to 69 8,463 1,563 645
10 age_gr=70 to 79 5,037 1,563 575
10 age_gr=80 or older 8,662 1,421 1,652
15 age_gr=18 to 39 26,382 827 3,571
15 age_gr=40 to 59 37,150 3,341 4,539
15 age_gr=60 to 69 7,928 1,660 1,129
15 age_gr=70 to 79 4,532 1,655 1,023
15 age_gr=80 or older 7,255 1,547 3,056
20 age_gr=18 to 39 24,620 850 5,370
20 age_gr=40 to 59 34,935 3,429 6,766
20 age_gr=60 to 69 7,360 1,690 1,699
20 age_gr=70 to 79 3,968 1,683 1,604
20 age_gr=80 or older 5,439 1,613 4,864
25 age_gr=18 to 39 22,379 852 7,371
25 age_gr=40 to 59 31,882 3,460 9,434
25 age_gr=60 to 69 6,653 1,698 2,296
25 age_gr=70 to 79 3,375 1,694 2,087
25 age_gr=80 or older 3,912 1,636 6,126
30 age_gr=18 to 39 20,052 855 10,355
30 age_gr=40 to 59 28,834 3,469 13,480
30 age_gr=60 to 69 5,976 1,705 3,202
30 age_gr=70 to 79 2,867 1,698 2,678
30 age_gr=80 or older 2,746 1,645 7,392
35 age_gr=18 to 39 17,309 855 13,056
35 age_gr=40 to 59 25,129 3,475 16,997
35 age_gr=60 to 69 5,157 1,707 3,996
35 age_gr=70 to 79 2,343 1,702 3,188
35 age_gr=80 or older 1,878 1,650 8,177
40 age_gr=18 to 39 13,773 856 16,688
40 age_gr=40 to 59 20,103 3,477 22,316
40 age_gr=60 to 69 4,042 1,707 5,190
40 age_gr=70 to 79 1,750 1,702 3,833
40 age_gr=80 or older 1,105 1,652 8,892
45 age_gr=18 to 39 7,315 856 22,439
45 age_gr=40 to 59 10,717 3,477 30,663
45 age_gr=60 to 69 1,982 1,708 6,973
45 age_gr=70 to 79 751 1,702 4,687
45 age_gr=80 or older 376 1,653 9,504
50 age_gr=18 to 39 3,343 857 27,135
50 age_gr=40 to 59 5,158 3,477 37,254
50 age_gr=60 to 69 878 1,708 8,226
50 age_gr=70 to 79 227 1,702 5,236
50 age_gr=80 or older 81 1,653 9,798
55 age_gr=18 to 39 526 857 29,201
55 age_gr=40 to 59 817 3,477 40,509
55 age_gr=60 to 69 158 1,708 8,766
55 age_gr=70 to 79 51 1,702 5,367
55 age_gr=80 or older 18 1,653 9,836
60 age_gr=18 to 39 61 857 29,462
60 age_gr=40 to 59 111 3,477 40,935
60 age_gr=60 to 69 32 1,708 8,856
60 age_gr=70 to 79 10 1,702 5,393
60 age_gr=80 or older 2 1,653 9,846
65 age_gr=18 to 39 16 857 29,519
65 age_gr=40 to 59 23 3,477 41,029
65 age_gr=60 to 69 1 1,708 8,885
65 age_gr=70 to 79 2 1,702 5,402
65 age_gr=80 or older 0 1,653 9,848

By charlson

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 charlson=0 81,206 0 0
0 charlson=1 10,635 0 0
0 charlson=2 8,161 0 0
0 charlson=3+ 8,556 0 0
5 charlson=0 77,101 2,945 1,970
5 charlson=1 9,781 640 372
5 charlson=2 7,316 748 253
5 charlson=3+ 7,495 888 384
10 charlson=0 71,161 4,934 5,463
10 charlson=1 8,736 1,016 978
10 charlson=2 6,408 1,141 705
10 charlson=3+ 6,324 1,308 1,066
15 charlson=0 67,499 5,319 9,424
15 charlson=1 8,034 1,111 1,673
15 charlson=2 5,841 1,215 1,247
15 charlson=3+ 5,407 1,423 1,947
20 charlson=0 62,883 5,442 14,108
20 charlson=1 7,135 1,148 2,578
20 charlson=2 5,141 1,240 1,973
20 charlson=3+ 4,329 1,473 3,021
25 charlson=0 56,963 5,485 19,313
25 charlson=1 6,186 1,156 3,372
25 charlson=2 4,405 1,254 2,573
25 charlson=3+ 3,362 1,485 3,815
30 charlson=0 50,935 5,504 27,099
30 charlson=1 5,322 1,157 4,420
30 charlson=2 3,770 1,259 3,328
30 charlson=3+ 2,643 1,492 4,646
35 charlson=0 43,951 5,508 33,863
35 charlson=1 4,502 1,157 5,207
35 charlson=2 3,149 1,265 3,929
35 charlson=3+ 1,962 1,499 5,258
40 charlson=0 34,822 5,511 43,389
40 charlson=1 3,474 1,157 6,298
40 charlson=2 2,413 1,266 4,674
40 charlson=3+ 1,329 1,500 5,880
45 charlson=0 18,158 5,512 58,135
45 charlson=1 1,757 1,157 7,775
45 charlson=2 1,216 1,266 5,731
45 charlson=3+ 569 1,501 6,516
50 charlson=0 8,297 5,513 69,732
50 charlson=1 797 1,157 8,877
50 charlson=2 508 1,266 6,519
50 charlson=3+ 218 1,501 6,893
55 charlson=0 1,325 5,513 74,848
55 charlson=1 128 1,157 9,388
55 charlson=2 101 1,266 6,831
55 charlson=3+ 35 1,501 7,029
60 charlson=0 186 5,513 75,522
60 charlson=1 14 1,157 9,464
60 charlson=2 16 1,266 6,882
60 charlson=3+ 3 1,501 7,052
65 charlson=0 37 5,513 75,686
65 charlson=1 3 1,157 9,478
65 charlson=2 3 1,266 6,895
65 charlson=3+ 0 1,501 7,055

By a_autoimmune_condition

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_autoimmune_condition=0 101,670 0 0
0 a_autoimmune_condition=1 6,888 0 0
5 a_autoimmune_condition=0 95,345 4,769 2,769
5 a_autoimmune_condition=1 6,348 452 210
10 a_autoimmune_condition=0 86,932 7,700 7,672
10 a_autoimmune_condition=1 5,697 699 540
15 a_autoimmune_condition=0 81,472 8,310 13,352
15 a_autoimmune_condition=1 5,309 758 939
20 a_autoimmune_condition=0 74,696 8,526 20,205
20 a_autoimmune_condition=1 4,792 777 1,475
25 a_autoimmune_condition=0 66,728 8,595 27,108
25 a_autoimmune_condition=1 4,188 785 1,965
30 a_autoimmune_condition=0 58,975 8,626 36,917
30 a_autoimmune_condition=1 3,695 786 2,576
35 a_autoimmune_condition=0 50,434 8,643 45,141
35 a_autoimmune_condition=1 3,130 786 3,116
40 a_autoimmune_condition=0 39,577 8,648 56,430
40 a_autoimmune_condition=1 2,461 786 3,811
45 a_autoimmune_condition=0 20,389 8,650 73,315
45 a_autoimmune_condition=1 1,311 786 4,842
50 a_autoimmune_condition=0 9,233 8,651 86,344
50 a_autoimmune_condition=1 587 786 5,677
55 a_autoimmune_condition=0 1,493 8,651 92,053
55 a_autoimmune_condition=1 96 786 6,043
60 a_autoimmune_condition=0 204 8,651 92,833
60 a_autoimmune_condition=1 15 786 6,087
65 a_autoimmune_condition=0 43 8,651 93,012
65 a_autoimmune_condition=1 0 786 6,102

By a_chronic_kidney_disease

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_chronic_kidney_disease=0 102,750 0 0
0 a_chronic_kidney_disease=1 5,808 0 0
5 a_chronic_kidney_disease=0 96,626 4,590 2,732
5 a_chronic_kidney_disease=1 5,067 631 247
10 a_chronic_kidney_disease=0 88,371 7,466 7,488
10 a_chronic_kidney_disease=1 4,258 933 724
15 a_chronic_kidney_disease=0 83,152 8,062 12,960
15 a_chronic_kidney_disease=1 3,629 1,006 1,331
20 a_chronic_kidney_disease=0 76,579 8,269 19,636
20 a_chronic_kidney_disease=1 2,909 1,034 2,044
25 a_chronic_kidney_disease=0 68,688 8,335 26,477
25 a_chronic_kidney_disease=1 2,228 1,045 2,596
30 a_chronic_kidney_disease=0 60,934 8,361 36,337
30 a_chronic_kidney_disease=1 1,736 1,051 3,156
35 a_chronic_kidney_disease=0 52,277 8,372 44,686
35 a_chronic_kidney_disease=1 1,287 1,057 3,571
40 a_chronic_kidney_disease=0 41,173 8,377 56,251
40 a_chronic_kidney_disease=1 865 1,057 3,990
45 a_chronic_kidney_disease=0 21,343 8,378 73,744
45 a_chronic_kidney_disease=1 357 1,058 4,413
50 a_chronic_kidney_disease=0 9,714 8,379 87,346
50 a_chronic_kidney_disease=1 106 1,058 4,675
55 a_chronic_kidney_disease=0 1,576 8,379 93,354
55 a_chronic_kidney_disease=1 13 1,058 4,742
60 a_chronic_kidney_disease=0 218 8,379 94,171
60 a_chronic_kidney_disease=1 1 1,058 4,749
65 a_chronic_kidney_disease=0 43 8,379 94,364
65 a_chronic_kidney_disease=1 0 1,058 4,750

By a_copd

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_copd=0 105,705 0 0
0 a_copd=1 2,853 0 0
5 a_copd=0 99,229 4,897 2,855
5 a_copd=1 2,464 324 124
10 a_copd=0 90,517 7,934 7,901
10 a_copd=1 2,112 465 311
15 a_copd=0 84,909 8,572 13,750
15 a_copd=1 1,872 496 541
20 a_copd=0 77,887 8,794 20,843
20 a_copd=1 1,601 509 837
25 a_copd=0 69,597 8,867 28,025
25 a_copd=1 1,319 513 1,048
30 a_copd=0 61,559 8,897 38,195
30 a_copd=1 1,111 515 1,298
35 a_copd=0 52,678 8,914 46,746
35 a_copd=1 886 515 1,511
40 a_copd=0 41,383 8,919 58,487
40 a_copd=1 655 515 1,754
45 a_copd=0 21,377 8,921 76,134
45 a_copd=1 323 515 2,023
50 a_copd=0 9,683 8,922 89,790
50 a_copd=1 137 515 2,231
55 a_copd=0 1,566 8,922 95,771
55 a_copd=1 23 515 2,325
60 a_copd=0 216 8,922 96,585
60 a_copd=1 3 515 2,335
65 a_copd=0 42 8,922 96,776
65 a_copd=1 1 515 2,338

By a_dementia

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_dementia=0 103,783 0 0
0 a_dementia=1 4,775 0 0
5 a_dementia=0 97,359 4,961 2,704
5 a_dementia=1 4,334 260 275
10 a_dementia=0 88,891 8,010 7,471
10 a_dementia=1 3,738 389 741
15 a_dementia=0 83,667 8,621 12,921
15 a_dementia=1 3,114 447 1,370
20 a_dementia=0 77,279 8,830 19,398
20 a_dementia=1 2,209 473 2,282
25 a_dementia=0 69,456 8,898 26,152
25 a_dementia=1 1,460 482 2,921
30 a_dementia=0 61,733 8,928 36,024
30 a_dementia=1 937 484 3,469
35 a_dementia=0 52,992 8,944 44,472
35 a_dementia=1 572 485 3,785
40 a_dementia=0 41,754 8,948 56,193
40 a_dementia=1 284 486 4,048
45 a_dementia=0 21,617 8,950 73,941
45 a_dementia=1 83 486 4,216
50 a_dementia=0 9,806 8,951 87,741
50 a_dementia=1 14 486 4,280
55 a_dementia=0 1,585 8,951 93,811
55 a_dementia=1 4 486 4,285
60 a_dementia=0 219 8,951 94,631
60 a_dementia=1 0 486 4,289
65 a_dementia=0 43 8,951 94,825
65 a_dementia=1 0 486 4,289

By a_heart_disease

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_heart_disease=0 95,108 0 0
0 a_heart_disease=1 13,450 0 0
5 a_heart_disease=0 89,677 3,984 2,511
5 a_heart_disease=1 12,016 1,237 468
10 a_heart_disease=0 82,233 6,518 6,881
10 a_heart_disease=1 10,396 1,881 1,331
15 a_heart_disease=0 77,523 7,030 11,865
15 a_heart_disease=1 9,258 2,038 2,426
20 a_heart_disease=0 71,544 7,199 17,933
20 a_heart_disease=1 7,944 2,104 3,747
25 a_heart_disease=0 64,261 7,265 24,274
25 a_heart_disease=1 6,655 2,115 4,799
30 a_heart_disease=0 57,085 7,288 33,436
30 a_heart_disease=1 5,585 2,124 6,057
35 a_heart_disease=0 48,999 7,300 41,230
35 a_heart_disease=1 4,565 2,129 7,027
40 a_heart_disease=0 38,635 7,303 51,999
40 a_heart_disease=1 3,403 2,131 8,242
45 a_heart_disease=0 20,082 7,304 68,398
45 a_heart_disease=1 1,618 2,132 9,759
50 a_heart_disease=0 9,139 7,305 81,206
50 a_heart_disease=1 681 2,132 10,815
55 a_heart_disease=0 1,462 7,305 86,863
55 a_heart_disease=1 127 2,132 11,233
60 a_heart_disease=0 210 7,305 87,610
60 a_heart_disease=1 9 2,132 11,310
65 a_heart_disease=0 42 7,305 87,796
65 a_heart_disease=1 1 2,132 11,318

By a_hyperlipidemia

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_hyperlipidemia=0 96,745 0 0
0 a_hyperlipidemia=1 11,813 0 0
5 a_hyperlipidemia=0 90,928 4,292 2,647
5 a_hyperlipidemia=1 10,765 929 332
10 a_hyperlipidemia=0 83,074 6,973 7,299
10 a_hyperlipidemia=1 9,555 1,426 913
15 a_hyperlipidemia=0 77,891 7,532 12,722
15 a_hyperlipidemia=1 8,890 1,536 1,569
20 a_hyperlipidemia=0 71,438 7,726 19,270
20 a_hyperlipidemia=1 8,050 1,577 2,410
25 a_hyperlipidemia=0 63,804 7,793 25,888
25 a_hyperlipidemia=1 7,112 1,587 3,185
30 a_hyperlipidemia=0 56,427 7,816 35,219
30 a_hyperlipidemia=1 6,243 1,596 4,274
35 a_hyperlipidemia=0 48,275 7,831 43,041
35 a_hyperlipidemia=1 5,289 1,598 5,216
40 a_hyperlipidemia=0 37,960 7,836 53,769
40 a_hyperlipidemia=1 4,078 1,598 6,472
45 a_hyperlipidemia=0 19,645 7,837 69,927
45 a_hyperlipidemia=1 2,055 1,599 8,230
50 a_hyperlipidemia=0 8,909 7,838 82,463
50 a_hyperlipidemia=1 911 1,599 9,558
55 a_hyperlipidemia=0 1,451 7,838 87,967
55 a_hyperlipidemia=1 138 1,599 10,129
60 a_hyperlipidemia=0 205 7,838 88,718
60 a_hyperlipidemia=1 14 1,599 10,202
65 a_hyperlipidemia=0 42 7,838 88,900
65 a_hyperlipidemia=1 1 1,599 10,214

By a_hypertension

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_hypertension=0 92,240 0 0
0 a_hypertension=1 16,318 0 0
5 a_hypertension=0 86,936 3,832 2,472
5 a_hypertension=1 14,757 1,389 507
10 a_hypertension=0 79,660 6,263 6,831
10 a_hypertension=1 12,969 2,136 1,381
15 a_hypertension=0 74,974 6,756 11,784
15 a_hypertension=1 11,807 2,312 2,507
20 a_hypertension=0 69,146 6,932 17,705
20 a_hypertension=1 10,342 2,371 3,975
25 a_hypertension=0 62,063 6,986 23,877
25 a_hypertension=1 8,853 2,394 5,196
30 a_hypertension=0 55,092 7,008 32,786
30 a_hypertension=1 7,578 2,404 6,707
35 a_hypertension=0 47,187 7,018 40,394
35 a_hypertension=1 6,377 2,411 7,863
40 a_hypertension=0 37,157 7,022 50,833
40 a_hypertension=1 4,881 2,412 9,408
45 a_hypertension=0 19,258 7,024 66,623
45 a_hypertension=1 2,442 2,412 11,534
50 a_hypertension=0 8,743 7,025 78,895
50 a_hypertension=1 1,077 2,412 13,126
55 a_hypertension=0 1,418 7,025 84,313
55 a_hypertension=1 171 2,412 13,783
60 a_hypertension=0 192 7,025 85,039
60 a_hypertension=1 27 2,412 13,881
65 a_hypertension=0 40 7,025 85,208
65 a_hypertension=1 3 2,412 13,906

By a_malignant_neoplasm

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_malignant_neoplasm=0 101,936 0 0
0 a_malignant_neoplasm=1 6,622 0 0
5 a_malignant_neoplasm=0 95,773 4,584 2,764
5 a_malignant_neoplasm=1 5,920 637 215
10 a_malignant_neoplasm=0 87,509 7,434 7,595
10 a_malignant_neoplasm=1 5,120 965 617
15 a_malignant_neoplasm=0 82,151 8,039 13,187
15 a_malignant_neoplasm=1 4,630 1,029 1,104
20 a_malignant_neoplasm=0 75,465 8,244 19,994
20 a_malignant_neoplasm=1 4,023 1,059 1,686
25 a_malignant_neoplasm=0 67,455 8,315 26,921
25 a_malignant_neoplasm=1 3,461 1,065 2,152
30 a_malignant_neoplasm=0 59,706 8,342 36,721
30 a_malignant_neoplasm=1 2,964 1,070 2,772
35 a_malignant_neoplasm=0 51,144 8,355 44,985
35 a_malignant_neoplasm=1 2,420 1,074 3,272
40 a_malignant_neoplasm=0 40,198 8,360 56,373
40 a_malignant_neoplasm=1 1,840 1,074 3,868
45 a_malignant_neoplasm=0 20,784 8,361 73,482
45 a_malignant_neoplasm=1 916 1,075 4,675
50 a_malignant_neoplasm=0 9,416 8,362 86,779
50 a_malignant_neoplasm=1 404 1,075 5,242
55 a_malignant_neoplasm=0 1,511 8,362 92,597
55 a_malignant_neoplasm=1 78 1,075 5,499
60 a_malignant_neoplasm=0 207 8,362 93,384
60 a_malignant_neoplasm=1 12 1,075 5,536
65 a_malignant_neoplasm=0 40 8,362 93,567
65 a_malignant_neoplasm=1 3 1,075 5,547

By a_obesity.5y

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_obesity.5y=0 86,219 0 0
0 a_obesity.5y=1 22,339 0 0
5 a_obesity.5y=0 81,448 3,293 2,385
5 a_obesity.5y=1 20,245 1,928 594
10 a_obesity.5y=0 74,764 5,427 6,535
10 a_obesity.5y=1 17,865 2,972 1,677
15 a_obesity.5y=0 70,210 5,898 11,372
15 a_obesity.5y=1 16,571 3,170 2,919
20 a_obesity.5y=0 64,437 6,060 17,201
20 a_obesity.5y=1 15,051 3,243 4,479
25 a_obesity.5y=0 57,672 6,112 23,104
25 a_obesity.5y=1 13,244 3,268 5,969
30 a_obesity.5y=0 51,036 6,132 31,485
30 a_obesity.5y=1 11,634 3,280 8,008
35 a_obesity.5y=0 43,740 6,145 38,520
35 a_obesity.5y=1 9,824 3,284 9,737
40 a_obesity.5y=0 34,461 6,149 48,095
40 a_obesity.5y=1 7,577 3,285 12,146
45 a_obesity.5y=0 17,987 6,151 62,679
45 a_obesity.5y=1 3,713 3,285 15,478
50 a_obesity.5y=0 8,286 6,151 74,068
50 a_obesity.5y=1 1,534 3,286 17,953
55 a_obesity.5y=0 1,328 6,151 79,213
55 a_obesity.5y=1 261 3,286 18,883
60 a_obesity.5y=0 178 6,151 79,906
60 a_obesity.5y=1 41 3,286 19,014
65 a_obesity.5y=0 35 6,151 80,063
65 a_obesity.5y=1 8 3,286 19,051

By a_t2_diabetes

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_t2_diabetes=0 101,182 0 0
0 a_t2_diabetes=1 7,376 0 0
5 a_t2_diabetes=0 95,248 4,377 2,719
5 a_t2_diabetes=1 6,445 844 260
10 a_t2_diabetes=0 87,154 7,126 7,504
10 a_t2_diabetes=1 5,475 1,273 708
15 a_t2_diabetes=0 81,891 7,701 13,019
15 a_t2_diabetes=1 4,890 1,367 1,272
20 a_t2_diabetes=0 75,297 7,902 19,715
20 a_t2_diabetes=1 4,191 1,401 1,965
25 a_t2_diabetes=0 67,413 7,971 26,535
25 a_t2_diabetes=1 3,503 1,409 2,538
30 a_t2_diabetes=0 59,718 7,996 36,312
30 a_t2_diabetes=1 2,952 1,416 3,181
35 a_t2_diabetes=0 51,144 8,009 44,560
35 a_t2_diabetes=1 2,420 1,420 3,697
40 a_t2_diabetes=0 40,250 8,012 55,916
40 a_t2_diabetes=1 1,788 1,422 4,325
45 a_t2_diabetes=0 20,890 8,014 72,975
45 a_t2_diabetes=1 810 1,422 5,182
50 a_t2_diabetes=0 9,501 8,015 86,305
50 a_t2_diabetes=1 319 1,422 5,716
55 a_t2_diabetes=0 1,531 8,015 92,182
55 a_t2_diabetes=1 58 1,422 5,914
60 a_t2_diabetes=0 215 8,015 92,970
60 a_t2_diabetes=1 4 1,422 5,950
65 a_t2_diabetes=0 43 8,015 93,160
65 a_t2_diabetes=1 0 1,422 5,954

Transition 5: From diagnosed with COVID-19 to death

By gender

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 gender=Male 44,565 0 0
0 gender=Female 63,993 0 0
5 gender=Male 41,060 147 3,998
5 gender=Female 60,633 207 3,848
10 gender=Male 36,965 387 7,517
10 gender=Female 55,664 554 8,153
15 gender=Male 34,632 635 9,898
15 gender=Female 52,149 909 11,917
20 gender=Male 31,995 831 12,452
20 gender=Female 47,493 1,194 16,506
25 gender=Male 28,740 955 15,159
25 gender=Female 42,176 1,389 20,950
30 gender=Male 25,654 1,045 19,032
30 gender=Female 37,016 1,481 27,347
35 gender=Male 22,249 1,100 22,263
35 gender=Female 31,315 1,554 32,769
40 gender=Male 17,693 1,133 27,079
40 gender=Female 24,345 1,592 39,871
45 gender=Male 9,102 1,160 34,599
45 gender=Female 12,598 1,609 50,225
50 gender=Male 4,068 1,165 40,518
50 gender=Female 5,752 1,621 58,154
55 gender=Male 594 1,169 43,018
55 gender=Female 995 1,624 61,722
60 gender=Male 65 1,169 43,338
60 gender=Female 154 1,625 62,225
65 gender=Male 7 1,169 43,395
65 gender=Female 36 1,625 62,362

By age

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 age_gr=Under 18 4,471 0 0
0 age_gr=18 to 39 30,379 0 0
0 age_gr=40 to 59 44,510 0 0
0 age_gr=60 to 69 10,593 0 0
0 age_gr=70 to 79 7,104 0 0
0 age_gr=80 or older 11,501 0 0
5 age_gr=Under 18 4,236 0 250
5 age_gr=18 to 39 29,363 1 1,175
5 age_gr=40 to 59 42,300 11 2,685
5 age_gr=60 to 69 9,558 18 1,218
5 age_gr=70 to 79 5,999 55 1,257
5 age_gr=80 or older 10,237 269 1,261
10 age_gr=Under 18 3,801 0 680
10 age_gr=18 to 39 27,661 1 2,806
10 age_gr=40 to 59 39,005 23 5,682
10 age_gr=60 to 69 8,463 46 2,162
10 age_gr=70 to 79 5,037 147 1,991
10 age_gr=80 or older 8,662 724 2,349
15 age_gr=Under 18 3,534 0 1,011
15 age_gr=18 to 39 26,382 1 4,397
15 age_gr=40 to 59 37,150 31 7,849
15 age_gr=60 to 69 7,928 72 2,717
15 age_gr=70 to 79 4,532 236 2,442
15 age_gr=80 or older 7,255 1,204 3,399
20 age_gr=Under 18 3,166 0 1,415
20 age_gr=18 to 39 24,620 1 6,219
20 age_gr=40 to 59 34,935 39 10,156
20 age_gr=60 to 69 7,360 88 3,301
20 age_gr=70 to 79 3,968 306 2,981
20 age_gr=80 or older 5,439 1,591 4,886
25 age_gr=Under 18 2,715 0 1,799
25 age_gr=18 to 39 22,379 1 8,222
25 age_gr=40 to 59 31,882 46 12,848
25 age_gr=60 to 69 6,653 103 3,891
25 age_gr=70 to 79 3,375 347 3,434
25 age_gr=80 or older 3,912 1,847 5,915
30 age_gr=Under 18 2,195 0 2,426
30 age_gr=18 to 39 20,052 2 11,208
30 age_gr=40 to 59 28,834 52 16,897
30 age_gr=60 to 69 5,976 110 4,797
30 age_gr=70 to 79 2,867 382 3,994
30 age_gr=80 or older 2,746 1,980 7,057
35 age_gr=Under 18 1,748 0 2,883
35 age_gr=18 to 39 17,309 2 13,909
35 age_gr=40 to 59 25,129 55 20,417
35 age_gr=60 to 69 5,157 120 5,583
35 age_gr=70 to 79 2,343 400 4,490
35 age_gr=80 or older 1,878 2,077 7,750
40 age_gr=Under 18 1,265 0 3,362
40 age_gr=18 to 39 13,773 4 17,540
40 age_gr=40 to 59 20,103 59 25,734
40 age_gr=60 to 69 4,042 125 6,772
40 age_gr=70 to 79 1,750 408 5,127
40 age_gr=80 or older 1,105 2,129 8,415
45 age_gr=Under 18 559 0 3,931
45 age_gr=18 to 39 7,315 5 23,290
45 age_gr=40 to 59 10,717 62 34,078
45 age_gr=60 to 69 1,982 129 8,552
45 age_gr=70 to 79 751 415 5,974
45 age_gr=80 or older 376 2,158 8,999
50 age_gr=Under 18 133 0 4,412
50 age_gr=18 to 39 3,343 5 27,987
50 age_gr=40 to 59 5,158 62 40,669
50 age_gr=60 to 69 878 132 9,802
50 age_gr=70 to 79 227 420 6,518
50 age_gr=80 or older 81 2,167 9,284
55 age_gr=Under 18 19 0 4,457
55 age_gr=18 to 39 526 5 30,053
55 age_gr=40 to 59 817 63 43,923
55 age_gr=60 to 69 158 135 10,339
55 age_gr=70 to 79 51 421 6,648
55 age_gr=80 or older 18 2,169 9,320
60 age_gr=Under 18 3 0 4,468
60 age_gr=18 to 39 61 5 30,314
60 age_gr=40 to 59 111 63 44,349
60 age_gr=60 to 69 32 136 10,428
60 age_gr=70 to 79 10 421 6,674
60 age_gr=80 or older 2 2,169 9,330
65 age_gr=Under 18 1 0 4,471
65 age_gr=18 to 39 16 5 30,371
65 age_gr=40 to 59 23 63 44,443
65 age_gr=60 to 69 1 136 10,457
65 age_gr=70 to 79 2 421 6,683
65 age_gr=80 or older 0 2,169 9,332

By charlson

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 charlson=0 81,206 0 0
0 charlson=1 10,635 0 0
0 charlson=2 8,161 0 0
0 charlson=3+ 8,556 0 0
5 charlson=0 77,101 43 4,872
5 charlson=1 9,781 81 931
5 charlson=2 7,316 58 943
5 charlson=3+ 7,495 172 1,100
10 charlson=0 71,161 123 10,274
10 charlson=1 8,736 196 1,798
10 charlson=2 6,408 167 1,679
10 charlson=3+ 6,324 455 1,919
15 charlson=0 67,499 212 14,531
15 charlson=1 8,034 318 2,466
15 charlson=2 5,841 264 2,198
15 charlson=3+ 5,407 750 2,620
20 charlson=0 62,883 275 19,275
20 charlson=1 7,135 418 3,308
20 charlson=2 5,141 353 2,860
20 charlson=3+ 4,329 979 3,515
25 charlson=0 56,963 309 24,489
25 charlson=1 6,186 483 4,045
25 charlson=2 4,405 416 3,411
25 charlson=3+ 3,362 1,136 4,164
30 charlson=0 50,935 341 32,262
30 charlson=1 5,322 517 5,060
30 charlson=2 3,770 457 4,130
30 charlson=3+ 2,643 1,211 4,927
35 charlson=0 43,951 361 39,010
35 charlson=1 4,502 544 5,820
35 charlson=2 3,149 482 4,712
35 charlson=3+ 1,962 1,267 5,490
40 charlson=0 34,822 375 48,525
40 charlson=1 3,474 555 6,900
40 charlson=2 2,413 495 5,445
40 charlson=3+ 1,329 1,300 6,080
45 charlson=0 18,158 384 63,263
45 charlson=1 1,757 563 8,369
45 charlson=2 1,216 504 6,493
45 charlson=3+ 569 1,318 6,699
50 charlson=0 8,297 389 74,856
50 charlson=1 797 564 9,470
50 charlson=2 508 506 7,279
50 charlson=3+ 218 1,327 7,067
55 charlson=0 1,325 391 79,970
55 charlson=1 128 565 9,980
55 charlson=2 101 507 7,590
55 charlson=3+ 35 1,330 7,200
60 charlson=0 186 391 80,644
60 charlson=1 14 566 10,055
60 charlson=2 16 507 7,641
60 charlson=3+ 3 1,330 7,223
65 charlson=0 37 391 80,808
65 charlson=1 3 566 10,069
65 charlson=2 3 507 7,654
65 charlson=3+ 0 1,330 7,226

By a_autoimmune_condition

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_autoimmune_condition=0 101,670 0 0
0 a_autoimmune_condition=1 6,888 0 0
5 a_autoimmune_condition=0 95,345 314 7,224
5 a_autoimmune_condition=1 6,348 40 622
10 a_autoimmune_condition=0 86,932 839 14,533
10 a_autoimmune_condition=1 5,697 102 1,137
15 a_autoimmune_condition=0 81,472 1,379 20,283
15 a_autoimmune_condition=1 5,309 165 1,532
20 a_autoimmune_condition=0 74,696 1,808 26,923
20 a_autoimmune_condition=1 4,792 217 2,035
25 a_autoimmune_condition=0 66,728 2,091 33,612
25 a_autoimmune_condition=1 4,188 253 2,497
30 a_autoimmune_condition=0 58,975 2,255 43,288
30 a_autoimmune_condition=1 3,695 271 3,091
35 a_autoimmune_condition=0 50,434 2,368 51,416
35 a_autoimmune_condition=1 3,130 286 3,616
40 a_autoimmune_condition=0 39,577 2,439 62,639
40 a_autoimmune_condition=1 2,461 286 4,311
45 a_autoimmune_condition=0 20,389 2,480 79,485
45 a_autoimmune_condition=1 1,311 289 5,339
50 a_autoimmune_condition=0 9,233 2,495 92,500
50 a_autoimmune_condition=1 587 291 6,172
55 a_autoimmune_condition=0 1,493 2,502 98,202
55 a_autoimmune_condition=1 96 291 6,538
60 a_autoimmune_condition=0 204 2,503 98,981
60 a_autoimmune_condition=1 15 291 6,582
65 a_autoimmune_condition=0 43 2,503 99,160
65 a_autoimmune_condition=1 0 291 6,597

By a_chronic_kidney_disease

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_chronic_kidney_disease=0 102,750 0 0
0 a_chronic_kidney_disease=1 5,808 0 0
5 a_chronic_kidney_disease=0 96,626 4,590 2,732
5 a_chronic_kidney_disease=1 5,067 631 247
10 a_chronic_kidney_disease=0 88,371 7,466 7,488
10 a_chronic_kidney_disease=1 4,258 933 724
15 a_chronic_kidney_disease=0 83,152 8,062 12,960
15 a_chronic_kidney_disease=1 3,629 1,006 1,331
20 a_chronic_kidney_disease=0 76,579 8,269 19,636
20 a_chronic_kidney_disease=1 2,909 1,034 2,044
25 a_chronic_kidney_disease=0 68,688 8,335 26,477
25 a_chronic_kidney_disease=1 2,228 1,045 2,596
30 a_chronic_kidney_disease=0 60,934 8,361 36,337
30 a_chronic_kidney_disease=1 1,736 1,051 3,156
35 a_chronic_kidney_disease=0 52,277 8,372 44,686
35 a_chronic_kidney_disease=1 1,287 1,057 3,571
40 a_chronic_kidney_disease=0 41,173 8,377 56,251
40 a_chronic_kidney_disease=1 865 1,057 3,990
45 a_chronic_kidney_disease=0 21,343 8,378 73,744
45 a_chronic_kidney_disease=1 357 1,058 4,413
50 a_chronic_kidney_disease=0 9,714 8,379 87,346
50 a_chronic_kidney_disease=1 106 1,058 4,675
55 a_chronic_kidney_disease=0 1,576 8,379 93,354
55 a_chronic_kidney_disease=1 13 1,058 4,742
60 a_chronic_kidney_disease=0 218 8,379 94,171
60 a_chronic_kidney_disease=1 1 1,058 4,749
65 a_chronic_kidney_disease=0 43 8,379 94,364
65 a_chronic_kidney_disease=1 0 1,058 4,750

By a_copd

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_copd=0 105,705 0 0
0 a_copd=1 2,853 0 0
5 a_copd=0 99,229 4,897 2,855
5 a_copd=1 2,464 324 124
10 a_copd=0 90,517 7,934 7,901
10 a_copd=1 2,112 465 311
15 a_copd=0 84,909 8,572 13,750
15 a_copd=1 1,872 496 541
20 a_copd=0 77,887 8,794 20,843
20 a_copd=1 1,601 509 837
25 a_copd=0 69,597 8,867 28,025
25 a_copd=1 1,319 513 1,048
30 a_copd=0 61,559 8,897 38,195
30 a_copd=1 1,111 515 1,298
35 a_copd=0 52,678 8,914 46,746
35 a_copd=1 886 515 1,511
40 a_copd=0 41,383 8,919 58,487
40 a_copd=1 655 515 1,754
45 a_copd=0 21,377 8,921 76,134
45 a_copd=1 323 515 2,023
50 a_copd=0 9,683 8,922 89,790
50 a_copd=1 137 515 2,231
55 a_copd=0 1,566 8,922 95,771
55 a_copd=1 23 515 2,325
60 a_copd=0 216 8,922 96,585
60 a_copd=1 3 515 2,335
65 a_copd=0 42 8,922 96,776
65 a_copd=1 1 515 2,338

By a_dementia

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_dementia=0 103,783 0 0
0 a_dementia=1 4,775 0 0
5 a_dementia=0 97,359 4,961 2,704
5 a_dementia=1 4,334 260 275
10 a_dementia=0 88,891 8,010 7,471
10 a_dementia=1 3,738 389 741
15 a_dementia=0 83,667 8,621 12,921
15 a_dementia=1 3,114 447 1,370
20 a_dementia=0 77,279 8,830 19,398
20 a_dementia=1 2,209 473 2,282
25 a_dementia=0 69,456 8,898 26,152
25 a_dementia=1 1,460 482 2,921
30 a_dementia=0 61,733 8,928 36,024
30 a_dementia=1 937 484 3,469
35 a_dementia=0 52,992 8,944 44,472
35 a_dementia=1 572 485 3,785
40 a_dementia=0 41,754 8,948 56,193
40 a_dementia=1 284 486 4,048
45 a_dementia=0 21,617 8,950 73,941
45 a_dementia=1 83 486 4,216
50 a_dementia=0 9,806 8,951 87,741
50 a_dementia=1 14 486 4,280
55 a_dementia=0 1,585 8,951 93,811
55 a_dementia=1 4 486 4,285
60 a_dementia=0 219 8,951 94,631
60 a_dementia=1 0 486 4,289
65 a_dementia=0 43 8,951 94,825
65 a_dementia=1 0 486 4,289

By a_heart_disease

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_heart_disease=0 95,108 0 0
0 a_heart_disease=1 13,450 0 0
5 a_heart_disease=0 89,677 3,984 2,511
5 a_heart_disease=1 12,016 1,237 468
10 a_heart_disease=0 82,233 6,518 6,881
10 a_heart_disease=1 10,396 1,881 1,331
15 a_heart_disease=0 77,523 7,030 11,865
15 a_heart_disease=1 9,258 2,038 2,426
20 a_heart_disease=0 71,544 7,199 17,933
20 a_heart_disease=1 7,944 2,104 3,747
25 a_heart_disease=0 64,261 7,265 24,274
25 a_heart_disease=1 6,655 2,115 4,799
30 a_heart_disease=0 57,085 7,288 33,436
30 a_heart_disease=1 5,585 2,124 6,057
35 a_heart_disease=0 48,999 7,300 41,230
35 a_heart_disease=1 4,565 2,129 7,027
40 a_heart_disease=0 38,635 7,303 51,999
40 a_heart_disease=1 3,403 2,131 8,242
45 a_heart_disease=0 20,082 7,304 68,398
45 a_heart_disease=1 1,618 2,132 9,759
50 a_heart_disease=0 9,139 7,305 81,206
50 a_heart_disease=1 681 2,132 10,815
55 a_heart_disease=0 1,462 7,305 86,863
55 a_heart_disease=1 127 2,132 11,233
60 a_heart_disease=0 210 7,305 87,610
60 a_heart_disease=1 9 2,132 11,310
65 a_heart_disease=0 42 7,305 87,796
65 a_heart_disease=1 1 2,132 11,318

By a_hyperlipidemia

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_hyperlipidemia=0 96,745 0 0
0 a_hyperlipidemia=1 11,813 0 0
5 a_hyperlipidemia=0 90,928 4,292 2,647
5 a_hyperlipidemia=1 10,765 929 332
10 a_hyperlipidemia=0 83,074 6,973 7,299
10 a_hyperlipidemia=1 9,555 1,426 913
15 a_hyperlipidemia=0 77,891 7,532 12,722
15 a_hyperlipidemia=1 8,890 1,536 1,569
20 a_hyperlipidemia=0 71,438 7,726 19,270
20 a_hyperlipidemia=1 8,050 1,577 2,410
25 a_hyperlipidemia=0 63,804 7,793 25,888
25 a_hyperlipidemia=1 7,112 1,587 3,185
30 a_hyperlipidemia=0 56,427 7,816 35,219
30 a_hyperlipidemia=1 6,243 1,596 4,274
35 a_hyperlipidemia=0 48,275 7,831 43,041
35 a_hyperlipidemia=1 5,289 1,598 5,216
40 a_hyperlipidemia=0 37,960 7,836 53,769
40 a_hyperlipidemia=1 4,078 1,598 6,472
45 a_hyperlipidemia=0 19,645 7,837 69,927
45 a_hyperlipidemia=1 2,055 1,599 8,230
50 a_hyperlipidemia=0 8,909 7,838 82,463
50 a_hyperlipidemia=1 911 1,599 9,558
55 a_hyperlipidemia=0 1,451 7,838 87,967
55 a_hyperlipidemia=1 138 1,599 10,129
60 a_hyperlipidemia=0 205 7,838 88,718
60 a_hyperlipidemia=1 14 1,599 10,202
65 a_hyperlipidemia=0 42 7,838 88,900
65 a_hyperlipidemia=1 1 1,599 10,214

By a_hypertension

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_hypertension=0 92,240 0 0
0 a_hypertension=1 16,318 0 0
5 a_hypertension=0 86,936 3,832 2,472
5 a_hypertension=1 14,757 1,389 507
10 a_hypertension=0 79,660 6,263 6,831
10 a_hypertension=1 12,969 2,136 1,381
15 a_hypertension=0 74,974 6,756 11,784
15 a_hypertension=1 11,807 2,312 2,507
20 a_hypertension=0 69,146 6,932 17,705
20 a_hypertension=1 10,342 2,371 3,975
25 a_hypertension=0 62,063 6,986 23,877
25 a_hypertension=1 8,853 2,394 5,196
30 a_hypertension=0 55,092 7,008 32,786
30 a_hypertension=1 7,578 2,404 6,707
35 a_hypertension=0 47,187 7,018 40,394
35 a_hypertension=1 6,377 2,411 7,863
40 a_hypertension=0 37,157 7,022 50,833
40 a_hypertension=1 4,881 2,412 9,408
45 a_hypertension=0 19,258 7,024 66,623
45 a_hypertension=1 2,442 2,412 11,534
50 a_hypertension=0 8,743 7,025 78,895
50 a_hypertension=1 1,077 2,412 13,126
55 a_hypertension=0 1,418 7,025 84,313
55 a_hypertension=1 171 2,412 13,783
60 a_hypertension=0 192 7,025 85,039
60 a_hypertension=1 27 2,412 13,881
65 a_hypertension=0 40 7,025 85,208
65 a_hypertension=1 3 2,412 13,906

By a_malignant_neoplasm

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_malignant_neoplasm=0 101,936 0 0
0 a_malignant_neoplasm=1 6,622 0 0
5 a_malignant_neoplasm=0 95,773 4,584 2,764
5 a_malignant_neoplasm=1 5,920 637 215
10 a_malignant_neoplasm=0 87,509 7,434 7,595
10 a_malignant_neoplasm=1 5,120 965 617
15 a_malignant_neoplasm=0 82,151 8,039 13,187
15 a_malignant_neoplasm=1 4,630 1,029 1,104
20 a_malignant_neoplasm=0 75,465 8,244 19,994
20 a_malignant_neoplasm=1 4,023 1,059 1,686
25 a_malignant_neoplasm=0 67,455 8,315 26,921
25 a_malignant_neoplasm=1 3,461 1,065 2,152
30 a_malignant_neoplasm=0 59,706 8,342 36,721
30 a_malignant_neoplasm=1 2,964 1,070 2,772
35 a_malignant_neoplasm=0 51,144 8,355 44,985
35 a_malignant_neoplasm=1 2,420 1,074 3,272
40 a_malignant_neoplasm=0 40,198 8,360 56,373
40 a_malignant_neoplasm=1 1,840 1,074 3,868
45 a_malignant_neoplasm=0 20,784 8,361 73,482
45 a_malignant_neoplasm=1 916 1,075 4,675
50 a_malignant_neoplasm=0 9,416 8,362 86,779
50 a_malignant_neoplasm=1 404 1,075 5,242
55 a_malignant_neoplasm=0 1,511 8,362 92,597
55 a_malignant_neoplasm=1 78 1,075 5,499
60 a_malignant_neoplasm=0 207 8,362 93,384
60 a_malignant_neoplasm=1 12 1,075 5,536
65 a_malignant_neoplasm=0 40 8,362 93,567
65 a_malignant_neoplasm=1 3 1,075 5,547

By a_obesity.5y

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_obesity.5y=0 86,219 0 0
0 a_obesity.5y=1 22,339 0 0
5 a_obesity.5y=0 81,448 3,293 2,385
5 a_obesity.5y=1 20,245 1,928 594
10 a_obesity.5y=0 74,764 5,427 6,535
10 a_obesity.5y=1 17,865 2,972 1,677
15 a_obesity.5y=0 70,210 5,898 11,372
15 a_obesity.5y=1 16,571 3,170 2,919
20 a_obesity.5y=0 64,437 6,060 17,201
20 a_obesity.5y=1 15,051 3,243 4,479
25 a_obesity.5y=0 57,672 6,112 23,104
25 a_obesity.5y=1 13,244 3,268 5,969
30 a_obesity.5y=0 51,036 6,132 31,485
30 a_obesity.5y=1 11,634 3,280 8,008
35 a_obesity.5y=0 43,740 6,145 38,520
35 a_obesity.5y=1 9,824 3,284 9,737
40 a_obesity.5y=0 34,461 6,149 48,095
40 a_obesity.5y=1 7,577 3,285 12,146
45 a_obesity.5y=0 17,987 6,151 62,679
45 a_obesity.5y=1 3,713 3,285 15,478
50 a_obesity.5y=0 8,286 6,151 74,068
50 a_obesity.5y=1 1,534 3,286 17,953
55 a_obesity.5y=0 1,328 6,151 79,213
55 a_obesity.5y=1 261 3,286 18,883
60 a_obesity.5y=0 178 6,151 79,906
60 a_obesity.5y=1 41 3,286 19,014
65 a_obesity.5y=0 35 6,151 80,063
65 a_obesity.5y=1 8 3,286 19,051

By a_t2_diabetes

Time since diagnosis Strata Number at risk Cumulative events Cumulative censored
0 a_t2_diabetes=0 101,182 0 0
0 a_t2_diabetes=1 7,376 0 0
5 a_t2_diabetes=0 95,248 4,377 2,719
5 a_t2_diabetes=1 6,445 844 260
10 a_t2_diabetes=0 87,154 7,126 7,504
10 a_t2_diabetes=1 5,475 1,273 708
15 a_t2_diabetes=0 81,891 7,701 13,019
15 a_t2_diabetes=1 4,890 1,367 1,272
20 a_t2_diabetes=0 75,297 7,902 19,715
20 a_t2_diabetes=1 4,191 1,401 1,965
25 a_t2_diabetes=0 67,413 7,971 26,535
25 a_t2_diabetes=1 3,503 1,409 2,538
30 a_t2_diabetes=0 59,718 7,996 36,312
30 a_t2_diabetes=1 2,952 1,416 3,181
35 a_t2_diabetes=0 51,144 8,009 44,560
35 a_t2_diabetes=1 2,420 1,420 3,697
40 a_t2_diabetes=0 40,250 8,012 55,916
40 a_t2_diabetes=1 1,788 1,422 4,325
45 a_t2_diabetes=0 20,890 8,014 72,975
45 a_t2_diabetes=1 810 1,422 5,182
50 a_t2_diabetes=0 9,501 8,015 86,305
50 a_t2_diabetes=1 319 1,422 5,716
55 a_t2_diabetes=0 1,531 8,015 92,182
55 a_t2_diabetes=1 58 1,422 5,914
60 a_t2_diabetes=0 215 8,015 92,970
60 a_t2_diabetes=1 4 1,422 5,950
65 a_t2_diabetes=0 43 8,015 93,160
65 a_t2_diabetes=1 0 1,422 5,954

Transition 6: From hospitalised with COVID-19 to death

By gender

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 gender=Male 9,872 0 0
0 gender=Female 8,104 0 0
5 gender=Male 9,711 97 117
5 gender=Female 7,947 85 124
10 gender=Male 9,316 358 288
10 gender=Female 7,552 280 346
15 gender=Male 8,772 682 547
15 gender=Female 7,064 526 639
20 gender=Male 8,123 966 924
20 gender=Female 6,531 683 1,044
25 gender=Male 7,424 1,159 1,414
25 gender=Female 5,867 798 1,562
30 gender=Male 6,571 1,323 2,234
30 gender=Female 5,028 892 2,417
35 gender=Male 5,474 1,430 3,233
35 gender=Female 4,089 966 3,303
40 gender=Male 3,850 1,546 4,944
40 gender=Female 2,881 1,020 4,514
45 gender=Male 1,683 1,615 6,807
45 gender=Female 1,347 1,072 5,846
50 gender=Male 537 1,660 7,825
50 gender=Female 432 1,111 6,676
55 gender=Male 133 1,666 8,114
55 gender=Female 109 1,118 6,902
60 gender=Male 31 1,669 8,177
60 gender=Female 39 1,119 6,951
65 gender=Male 4 1,669 8,203
65 gender=Female 7 1,122 6,982

By age

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 age_gr=18 to 39 1,283 0 0
0 age_gr=40 to 59 5,271 0 0
0 age_gr=60 to 69 3,297 0 0
0 age_gr=70 to 79 3,949 0 0
0 age_gr=80 or older 4,103 0 0
5 age_gr=18 to 39 1,267 0 20
5 age_gr=40 to 59 5,221 13 52
5 age_gr=60 to 69 3,264 8 34
5 age_gr=70 to 79 3,897 40 32
5 age_gr=80 or older 3,939 121 99
10 age_gr=18 to 39 1,225 2 62
10 age_gr=40 to 59 5,121 22 144
10 age_gr=60 to 69 3,193 40 83
10 age_gr=70 to 79 3,734 155 105
10 age_gr=80 or older 3,532 419 230
15 age_gr=18 to 39 1,171 3 126
15 age_gr=40 to 59 4,971 48 293
15 age_gr=60 to 69 3,089 86 144
15 age_gr=70 to 79 3,488 317 200
15 age_gr=80 or older 3,057 754 410
20 age_gr=18 to 39 1,099 5 194
20 age_gr=40 to 59 4,749 71 521
20 age_gr=60 to 69 2,965 128 259
20 age_gr=70 to 79 3,232 466 317
20 age_gr=80 or older 2,556 979 657
25 age_gr=18 to 39 1,010 6 285
25 age_gr=40 to 59 4,396 92 842
25 age_gr=60 to 69 2,767 169 403
25 age_gr=70 to 79 2,961 569 471
25 age_gr=80 or older 2,110 1,120 948
30 age_gr=18 to 39 880 8 443
30 age_gr=40 to 59 3,874 107 1,471
30 age_gr=60 to 69 2,490 209 686
30 age_gr=70 to 79 2,644 655 733
30 age_gr=80 or older 1,675 1,235 1,281
35 age_gr=18 to 39 710 8 598
35 age_gr=40 to 59 3,134 123 2,208
35 age_gr=60 to 69 2,090 240 1,068
35 age_gr=70 to 79 2,283 698 1,072
35 age_gr=80 or older 1,317 1,326 1,544
40 age_gr=18 to 39 492 9 822
40 age_gr=40 to 59 2,152 133 3,249
40 age_gr=60 to 69 1,533 269 1,679
40 age_gr=70 to 79 1,650 761 1,737
40 age_gr=80 or older 882 1,393 1,920
45 age_gr=18 to 39 239 10 1,066
45 age_gr=40 to 59 935 140 4,333
45 age_gr=60 to 69 697 283 2,412
45 age_gr=70 to 79 724 798 2,508
45 age_gr=80 or older 423 1,455 2,273
50 age_gr=18 to 39 82 11 1,213
50 age_gr=40 to 59 277 141 4,930
50 age_gr=60 to 69 199 294 2,852
50 age_gr=70 to 79 247 834 2,944
50 age_gr=80 or older 158 1,490 2,496
55 age_gr=18 to 39 17 11 1,259
55 age_gr=40 to 59 67 142 5,078
55 age_gr=60 to 69 49 296 2,972
55 age_gr=70 to 79 55 838 3,069
55 age_gr=80 or older 52 1,496 2,566
60 age_gr=18 to 39 5 11 1,267
60 age_gr=40 to 59 22 143 5,110
60 age_gr=60 to 69 9 296 2,993
60 age_gr=70 to 79 16 840 3,095
60 age_gr=80 or older 18 1,497 2,591
65 age_gr=18 to 39 2 11 1,272
65 age_gr=40 to 59 4 143 5,128
65 age_gr=60 to 69 0 296 3,001
65 age_gr=70 to 79 3 840 3,109
65 age_gr=80 or older 2 1,500 2,603

By charlson

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 charlson=0 9,233 0 0
0 charlson=1 2,310 0 0
0 charlson=2 2,779 0 0
0 charlson=3+ 3,654 0 0
5 charlson=0 9,132 35 96
5 charlson=1 2,256 32 41
5 charlson=2 2,723 36 33
5 charlson=3+ 3,547 79 71
10 charlson=0 8,899 124 263
10 charlson=1 2,135 95 103
10 charlson=2 2,573 138 102
10 charlson=3+ 3,261 281 166
15 charlson=0 8,566 228 524
15 charlson=1 1,971 194 180
15 charlson=2 2,393 258 176
15 charlson=3+ 2,906 528 306
20 charlson=0 8,154 311 890
20 charlson=1 1,798 275 281
20 charlson=2 2,188 349 291
20 charlson=3+ 2,514 714 506
25 charlson=0 7,561 385 1,394
25 charlson=1 1,635 315 401
25 charlson=2 1,966 406 442
25 charlson=3+ 2,129 851 739
30 charlson=0 6,713 457 2,338
30 charlson=1 1,390 361 616
30 charlson=2 1,716 445 687
30 charlson=3+ 1,780 952 1,010
35 charlson=0 5,567 491 3,489
35 charlson=1 1,165 392 813
35 charlson=2 1,411 487 952
35 charlson=3+ 1,420 1,026 1,282
40 charlson=0 3,912 539 5,255
40 charlson=1 837 414 1,149
40 charlson=2 982 521 1,379
40 charlson=3+ 1,000 1,092 1,675
45 charlson=0 1,720 577 7,182
45 charlson=1 369 433 1,554
45 charlson=2 466 545 1,822
45 charlson=3+ 475 1,132 2,095
50 charlson=0 480 601 8,292
50 charlson=1 142 444 1,761
50 charlson=2 164 565 2,092
50 charlson=3+ 183 1,161 2,356
55 charlson=0 100 603 8,563
55 charlson=1 39 444 1,838
55 charlson=2 46 571 2,176
55 charlson=3+ 57 1,166 2,439
60 charlson=0 26 604 8,606
60 charlson=1 13 445 1,852
60 charlson=2 10 572 2,199
60 charlson=3+ 21 1,167 2,471
65 charlson=0 4 604 8,629
65 charlson=1 2 447 1,863
65 charlson=2 3 572 2,207
65 charlson=3+ 2 1,168 2,486

By a_autoimmune_condition

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_autoimmune_condition=0 16,323 0 0
0 a_autoimmune_condition=1 1,653 0 0
5 a_autoimmune_condition=0 16,031 170 223
5 a_autoimmune_condition=1 1,627 12 18
10 a_autoimmune_condition=0 15,317 579 583
10 a_autoimmune_condition=1 1,551 59 51
15 a_autoimmune_condition=0 14,392 1,068 1,085
15 a_autoimmune_condition=1 1,444 140 101
20 a_autoimmune_condition=0 13,360 1,437 1,788
20 a_autoimmune_condition=1 1,294 212 180
25 a_autoimmune_condition=0 12,140 1,710 2,703
25 a_autoimmune_condition=1 1,151 247 273
30 a_autoimmune_condition=0 10,598 1,936 4,238
30 a_autoimmune_condition=1 1,001 279 413
35 a_autoimmune_condition=0 8,749 2,097 5,954
35 a_autoimmune_condition=1 814 299 582
40 a_autoimmune_condition=0 6,173 2,253 8,625
40 a_autoimmune_condition=1 558 313 833
45 a_autoimmune_condition=0 2,765 2,359 11,562
45 a_autoimmune_condition=1 265 328 1,091
50 a_autoimmune_condition=0 879 2,434 13,247
50 a_autoimmune_condition=1 90 337 1,254
55 a_autoimmune_condition=0 213 2,446 13,724
55 a_autoimmune_condition=1 29 338 1,292
60 a_autoimmune_condition=0 59 2,449 13,824
60 a_autoimmune_condition=1 11 339 1,304
65 a_autoimmune_condition=0 9 2,451 13,872
65 a_autoimmune_condition=1 2 340 1,313

By a_chronic_kidney_disease

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_chronic_kidney_disease=0 15,377 0 0
0 a_chronic_kidney_disease=1 2,599 0 0
5 a_chronic_kidney_disease=0 15,129 136 192
5 a_chronic_kidney_disease=1 2,529 46 49
10 a_chronic_kidney_disease=0 14,540 448 521
10 a_chronic_kidney_disease=1 2,328 190 113
15 a_chronic_kidney_disease=0 13,750 832 979
15 a_chronic_kidney_disease=1 2,086 376 207
20 a_chronic_kidney_disease=0 12,863 1,128 1,632
20 a_chronic_kidney_disease=1 1,791 521 336
25 a_chronic_kidney_disease=0 11,757 1,338 2,497
25 a_chronic_kidney_disease=1 1,534 619 479
30 a_chronic_kidney_disease=0 10,311 1,524 3,971
30 a_chronic_kidney_disease=1 1,288 691 680
35 a_chronic_kidney_disease=0 8,520 1,657 5,655
35 a_chronic_kidney_disease=1 1,043 739 881
40 a_chronic_kidney_disease=0 6,043 1,770 8,268
40 a_chronic_kidney_disease=1 688 796 1,190
45 a_chronic_kidney_disease=0 2,703 1,860 11,180
45 a_chronic_kidney_disease=1 327 827 1,473
50 a_chronic_kidney_disease=0 846 1,921 12,842
50 a_chronic_kidney_disease=1 123 850 1,659
55 a_chronic_kidney_disease=0 204 1,928 13,305
55 a_chronic_kidney_disease=1 38 856 1,711
60 a_chronic_kidney_disease=0 58 1,931 13,396
60 a_chronic_kidney_disease=1 12 857 1,732
65 a_chronic_kidney_disease=0 11 1,933 13,444
65 a_chronic_kidney_disease=1 0 858 1,741

By a_copd

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_copd=0 16,663 0 0
0 a_copd=1 1,313 0 0
5 a_copd=0 16,370 166 222
5 a_copd=1 1,288 16 19
10 a_copd=0 15,662 562 579
10 a_copd=1 1,206 76 55
15 a_copd=0 14,753 1,055 1,083
15 a_copd=1 1,083 153 103
20 a_copd=0 13,705 1,426 1,800
20 a_copd=1 949 223 168
25 a_copd=0 12,471 1,688 2,726
25 a_copd=1 820 269 250
30 a_copd=0 10,905 1,903 4,311
30 a_copd=1 694 312 340
35 a_copd=0 8,980 2,064 6,110
35 a_copd=1 583 332 426
40 a_copd=0 6,313 2,211 8,882
40 a_copd=1 418 355 576
45 a_copd=0 2,815 2,319 11,907
45 a_copd=1 215 368 746
50 a_copd=0 877 2,394 13,637
50 a_copd=1 92 377 864
55 a_copd=0 218 2,406 14,100
55 a_copd=1 24 378 916
60 a_copd=0 60 2,409 14,203
60 a_copd=1 10 379 925
65 a_copd=0 9 2,412 14,251
65 a_copd=1 2 379 934

By a_dementia

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_dementia=0 16,893 0 0
0 a_dementia=1 1,083 0 0
5 a_dementia=0 16,633 147 198
5 a_dementia=1 1,025 35 43
10 a_dementia=0 15,969 514 545
10 a_dementia=1 899 124 89
15 a_dementia=0 15,103 988 1,022
15 a_dementia=1 733 220 164
20 a_dementia=0 14,064 1,373 1,719
20 a_dementia=1 590 276 249
25 a_dementia=0 12,840 1,638 2,638
25 a_dementia=1 451 319 338
30 a_dementia=0 11,282 1,861 4,211
30 a_dementia=1 317 354 440
35 a_dementia=0 9,342 2,018 6,032
35 a_dementia=1 221 378 504
40 a_dementia=0 6,591 2,170 8,893
40 a_dementia=1 140 396 565
45 a_dementia=0 2,952 2,282 12,047
45 a_dementia=1 78 405 606
50 a_dementia=0 944 2,355 13,852
50 a_dementia=1 25 416 649
55 a_dementia=0 234 2,368 14,357
55 a_dementia=1 8 416 659
60 a_dementia=0 68 2,372 14,463
60 a_dementia=1 2 416 665
65 a_dementia=0 10 2,375 14,518
65 a_dementia=1 1 416 667

By a_heart_disease

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_heart_disease=0 13,006 0 0
0 a_heart_disease=1 4,970 0 0
5 a_heart_disease=0 12,824 89 151
5 a_heart_disease=1 4,834 93 90
10 a_heart_disease=0 12,372 299 429
10 a_heart_disease=1 4,496 339 205
15 a_heart_disease=0 11,748 572 828
15 a_heart_disease=1 4,088 636 358
20 a_heart_disease=0 11,027 792 1,381
20 a_heart_disease=1 3,627 857 587
25 a_heart_disease=0 10,120 952 2,099
25 a_heart_disease=1 3,171 1,005 877
30 a_heart_disease=0 8,917 1,098 3,373
30 a_heart_disease=1 2,682 1,117 1,278
35 a_heart_disease=0 7,357 1,199 4,856
35 a_heart_disease=1 2,206 1,197 1,680
40 a_heart_disease=0 5,191 1,295 7,153
40 a_heart_disease=1 1,540 1,271 2,305
45 a_heart_disease=0 2,303 1,367 9,648
45 a_heart_disease=1 727 1,320 3,005
50 a_heart_disease=0 723 1,409 11,077
50 a_heart_disease=1 246 1,362 3,424
55 a_heart_disease=0 170 1,417 11,471
55 a_heart_disease=1 72 1,367 3,545
60 a_heart_disease=0 47 1,419 11,546
60 a_heart_disease=1 23 1,369 3,582
65 a_heart_disease=0 9 1,420 11,586
65 a_heart_disease=1 2 1,371 3,599

By a_hyperlipidemia

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_hyperlipidemia=0 14,793 0 0
0 a_hyperlipidemia=1 3,183 0 0
5 a_hyperlipidemia=0 14,525 150 203
5 a_hyperlipidemia=1 3,133 32 38
10 a_hyperlipidemia=0 13,849 541 546
10 a_hyperlipidemia=1 3,019 97 88
15 a_hyperlipidemia=0 12,991 1,012 1,001
15 a_hyperlipidemia=1 2,845 196 185
20 a_hyperlipidemia=0 12,005 1,359 1,661
20 a_hyperlipidemia=1 2,649 290 307
25 a_hyperlipidemia=0 10,872 1,621 2,520
25 a_hyperlipidemia=1 2,419 336 456
30 a_hyperlipidemia=0 9,462 1,827 3,905
30 a_hyperlipidemia=1 2,137 388 746
35 a_hyperlipidemia=0 7,783 1,968 5,471
35 a_hyperlipidemia=1 1,780 428 1,065
40 a_hyperlipidemia=0 5,460 2,100 7,852
40 a_hyperlipidemia=1 1,271 466 1,606
45 a_hyperlipidemia=0 2,469 2,190 10,441
45 a_hyperlipidemia=1 561 497 2,212
50 a_hyperlipidemia=0 796 2,252 11,960
50 a_hyperlipidemia=1 173 519 2,541
55 a_hyperlipidemia=0 205 2,262 12,380
55 a_hyperlipidemia=1 37 522 2,636
60 a_hyperlipidemia=0 61 2,266 12,475
60 a_hyperlipidemia=1 9 522 2,653
65 a_hyperlipidemia=0 10 2,268 12,525
65 a_hyperlipidemia=1 1 523 2,660

By a_hypertension

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_hypertension=0 12,962 0 0
0 a_hypertension=1 5,014 0 0
5 a_hypertension=0 12,765 123 155
5 a_hypertension=1 4,893 59 86
10 a_hypertension=0 12,211 419 450
10 a_hypertension=1 4,657 219 184
15 a_hypertension=0 11,506 800 843
15 a_hypertension=1 4,330 408 343
20 a_hypertension=0 10,678 1,088 1,398
20 a_hypertension=1 3,976 561 570
25 a_hypertension=0 9,722 1,294 2,124
25 a_hypertension=1 3,569 663 852
30 a_hypertension=0 8,461 1,459 3,409
30 a_hypertension=1 3,138 756 1,242
35 a_hypertension=0 6,933 1,574 4,847
35 a_hypertension=1 2,630 822 1,689
40 a_hypertension=0 4,869 1,673 6,979
40 a_hypertension=1 1,862 893 2,479
45 a_hypertension=0 2,192 1,754 9,294
45 a_hypertension=1 838 933 3,359
50 a_hypertension=0 699 1,812 10,639
50 a_hypertension=1 270 959 3,862
55 a_hypertension=0 181 1,818 11,012
55 a_hypertension=1 61 966 4,004
60 a_hypertension=0 58 1,821 11,091
60 a_hypertension=1 12 967 4,037
65 a_hypertension=0 10 1,822 11,140
65 a_hypertension=1 1 969 4,045

By a_malignant_neoplasm

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_malignant_neoplasm=0 15,498 0 0
0 a_malignant_neoplasm=1 2,478 0 0
5 a_malignant_neoplasm=0 15,241 137 204
5 a_malignant_neoplasm=1 2,417 45 37
10 a_malignant_neoplasm=0 14,609 476 542
10 a_malignant_neoplasm=1 2,259 162 92
15 a_malignant_neoplasm=0 13,773 916 1,020
15 a_malignant_neoplasm=1 2,063 292 166
20 a_malignant_neoplasm=0 12,799 1,246 1,707
20 a_malignant_neoplasm=1 1,855 403 261
25 a_malignant_neoplasm=0 11,653 1,477 2,576
25 a_malignant_neoplasm=1 1,638 480 400
30 a_malignant_neoplasm=0 10,182 1,677 4,069
30 a_malignant_neoplasm=1 1,417 538 582
35 a_malignant_neoplasm=0 8,405 1,803 5,760
35 a_malignant_neoplasm=1 1,158 593 776
40 a_malignant_neoplasm=0 5,886 1,929 8,368
40 a_malignant_neoplasm=1 845 637 1,090
45 a_malignant_neoplasm=0 2,630 2,022 11,193
45 a_malignant_neoplasm=1 400 665 1,460
50 a_malignant_neoplasm=0 823 2,088 12,819
50 a_malignant_neoplasm=1 146 683 1,682
55 a_malignant_neoplasm=0 201 2,095 13,259
55 a_malignant_neoplasm=1 41 689 1,757
60 a_malignant_neoplasm=0 58 2,097 13,350
60 a_malignant_neoplasm=1 12 691 1,778
65 a_malignant_neoplasm=0 8 2,100 13,398
65 a_malignant_neoplasm=1 3 691 1,787

By a_obesity.5y

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_obesity.5y=0 11,320 0 0
0 a_obesity.5y=1 6,656 0 0
5 a_obesity.5y=0 11,108 119 164
5 a_obesity.5y=1 6,550 63 77
10 a_obesity.5y=0 10,581 409 442
10 a_obesity.5y=1 6,287 229 192
15 a_obesity.5y=0 9,904 754 815
15 a_obesity.5y=1 5,932 454 371
20 a_obesity.5y=0 9,163 1,010 1,325
20 a_obesity.5y=1 5,491 639 643
25 a_obesity.5y=0 8,335 1,175 1,969
25 a_obesity.5y=1 4,956 782 1,007
30 a_obesity.5y=0 7,232 1,334 3,060
30 a_obesity.5y=1 4,367 881 1,591
35 a_obesity.5y=0 5,948 1,441 4,285
35 a_obesity.5y=1 3,615 955 2,251
40 a_obesity.5y=0 4,141 1,536 6,119
40 a_obesity.5y=1 2,590 1,030 3,339
45 a_obesity.5y=0 1,896 1,617 8,064
45 a_obesity.5y=1 1,134 1,070 4,589
50 a_obesity.5y=0 621 1,661 9,206
50 a_obesity.5y=1 348 1,110 5,295
55 a_obesity.5y=0 157 1,671 9,542
55 a_obesity.5y=1 85 1,113 5,474
60 a_obesity.5y=0 41 1,672 9,613
60 a_obesity.5y=1 29 1,116 5,515
65 a_obesity.5y=0 8 1,673 9,647
65 a_obesity.5y=1 3 1,118 5,538

By a_t2_diabetes

Time since hospitalisation Strata Number at risk Cumulative events Cumulative censored
0 a_t2_diabetes=0 14,786 0 0
0 a_t2_diabetes=1 3,190 0 0
5 a_t2_diabetes=0 14,532 130 209
5 a_t2_diabetes=1 3,126 52 32
10 a_t2_diabetes=0 13,917 467 534
10 a_t2_diabetes=1 2,951 171 100
15 a_t2_diabetes=0 13,104 883 993
15 a_t2_diabetes=1 2,732 325 193
20 a_t2_diabetes=0 12,183 1,198 1,638
20 a_t2_diabetes=1 2,471 451 330
25 a_t2_diabetes=0 11,105 1,421 2,455
25 a_t2_diabetes=1 2,186 536 521
30 a_t2_diabetes=0 9,724 1,612 3,849
30 a_t2_diabetes=1 1,875 603 802
35 a_t2_diabetes=0 8,026 1,752 5,445
35 a_t2_diabetes=1 1,537 644 1,091
40 a_t2_diabetes=0 5,674 1,885 7,876
40 a_t2_diabetes=1 1,057 681 1,582
45 a_t2_diabetes=0 2,558 1,978 10,588
45 a_t2_diabetes=1 472 709 2,065
50 a_t2_diabetes=0 799 2,042 12,172
50 a_t2_diabetes=1 170 729 2,329
55 a_t2_diabetes=0 198 2,051 12,592
55 a_t2_diabetes=1 44 733 2,424
60 a_t2_diabetes=0 61 2,055 12,678
60 a_t2_diabetes=1 9 733 2,450
65 a_t2_diabetes=0 10 2,057 12,729
65 a_t2_diabetes=1 1 734 2,456